Center for Biomedical Engineering

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Reengineering the Medical Equipment Management system the Provider Regulator Purchaser Aspect
(Addis Ababa University, 2014-08) Hussein, Ashenafi; Assefa, Birhanu (MSc)
A very poor management of healthcare equipment results wastage of valuable limited resources. A well designed and properly implemented Medical equipment management system can help not only managing the financial resources but also improves the quality of life. Different kinds of models are used in many developed countries to reengineer system. Although conceptual model is easy to understand with less accuracy and simulation model is more accurate modeling; neither of them are good to model the strategic implication. The new design is developed based on the concept of input output mechanism engineering model. The thesis evaluated the existing Medical equipment management practices and processes and reengineers the system and come up with a new design to solve the existing problems. In order to properly assign the main inputs in the system design, a preliminary study was implemented in Addis Ababa, Ethiopia to assess the main challenges in healthcare providing organizations. In this preliminary study a total 243 number of healthcare providers were selected including hospitals, health centers and health clinics. Healthcare professionals from those major health facilities were interviewed and asked to full a questionnaire on existing healthcare technology management. The data collected from the facilities indicates there is a poor performance in provision (did not have system to forecast equipment demand based on available budget that was 67.8% of hospitals and 61.1% of health centers), acquisition (68.8% of hospitals, 77.8% of health centers and clinics, 63.6%were found out to be without guideline for procurement), utilization (75.0% of hospitals and 50.0% of health centers in Addis Ababa, are not properly installed), donation (83.3% of them reported that they do not have system to track the donation) and decommissioning (56.2% of hospitals, 66.7% of health centers and 56.9% of clinics reported that they have a problem in decontaminating devices before use and after removal) of medical equipment. The results offered the opportunity to appraise a reengineering the system design of the overall healthcare technology system and design a system to reduce the problem that exists to show improvement for increased efficiency and more effective utilization of available resources. An input output model, the provider regulator purchaser model is used to construct the detail design of the healthcare technology system. MIS and desktop software application is developed for the new designed system. The challenges encountered in this thesis included limited number of samples, difficulty to collect questionnaires and testing the system in the real world. As the thesis includes designs from the strategic planning until decommissioning, it can be used as a blue print from where health facilities, regulatory authorities, purchasing entities can implement.
An Automated Segmentation at Retinal Images for Use in Diabetic Retinopathy Studies
(Addis Ababa University, 2014-10) Moges, Daniel; Assefa, Dawit (PhD.)
Automated computer aided detection of retinal lesions associated with Diabetic Retinopathy (DR) offers many potential benefits. In a screening setting, it allows the examination of large number of images in less time and more objectively than traditional observer driven techniques. In a clinical setting, it can be an important diagnostic aid by reducing the workload of trained graders and other costs. However, the segmentation of major pathological structures and their subsequent follow–ups are not easy because of various artifacts such as presence of anatomical structures with highly correlated pixels with that of lesion, illumination variability, noise and movement of the eye during multiple visits by the patient. This study presents a novel mathematical scheme for analysis of color retinal images acquired through digital fundus cameras from patients treated for DR. The proposed scheme uses a holistic representation of the color images in the three (trinion) space and applies trinion based Fourier transforms to extract useful imaging features for the purpose of classification and segmentation of retinal images. A suitable color space transformation and a way of extracting robust higher order features are included in the method. The scheme has been applied in analyzing images acquired from standard retinal image databases. Results have showed that the algorithm achieved 86.06% sensitivity, 96.06% specificity, and 92.65% accuracy for pixel base segmentation of Hard Exudates (HEs) the most prevalent lesions that appears in the earliest stages of DR, while it achieved 96.67% sensitivity, 100% specificity and 97.3% accuracy for image base classification of abnormalities due to DR.
Medical Image Segmentation Using Spectral Clustering Based on Hypercomplex Analysis
(Addis Ababa University, 2016-10) Getaneh, Samuel; Assefa, Dawit (PhD.)
Images are destined to be segmented, either with our visual system (as we do it every day) or algorithmically using computing machines. However, as critical as medical images, image segmentation has to be consistent which we do not usually have. Therefore, to bring consistency, physicians need some kind of consistent clues about medical images they are analyzing. Particularly, when the images considered are colors with multiple channels/bands, developing such a consistent segmentation tool could take more effort and that often requires a rigorous mathematical computation and algorithm free from human cognition. To create such algorithm for color medical image segmentation, primarily the images have to be considered as collection of pixels that have to be represented properly and holistically with no separation of color components. Secondly, the relationships of pixels have to be defined holistically. Finally, based on these defined relationships, the pixels have to be grouped and segmented. Hence, in order to perform holistic segmentation, quaternion based representation of color pixels and quaternion based spectral clustering technique has been proposed in this thesis. Test results have shown that the proposed scheme can use and also can be used in machine learning applications of image segmentation and pattern recognition. Keywords: Quaternion Rotation, Spectral Clustering, Image Processing, Color Distance Measurement
Robust Edge Detection Applied to Multi-Parametric Magnetic Resonance Images
(Addis Ababa University, 2016-11) Damenu, Serkalem; Assefa, Dawit (PhD.)
Various edge detection techniques for color images that have been proposed in the last two decades showed that color images contain 10% additional edge information as compared to their gray scale counterparts. Edge detection is one of the most commonly used operations in medical color image processing. Efficient and accurate edge detection leads to increased performance of subsequent image processing techniques, including image segmentation, object-based image coding, and image retrieval. A color image edge detection algorithm is proposed in this paper that introduces a robust and automated scheme that makes use of higher order statistical features derived from locally computed trinion Fourier transforms. The proposed scheme uses a holistic vectorial representation of the color images in the three (trinion) space and applies trinion based Fourier transforms to extract useful imaging features for the purpose of edge detection of multi parametric magnetic resonance images (MP-MRI). A suitable color space transformation and a way of extracting robust higher order features are included in the method. Performance of the proposed scheme is compared against classical edge detection methods and other vectorial approaches which have been proposed in the literature. Results have shown that none of the classical as well as the other vectorial approaches were able to detect useful edges. Application of the method is shown in edge detection on MP-MR images of brain scans of patients treated for Glioblastoma multiforme (GBM). The algorithm performs well in detecting the tumor edges, that was (qualitatively) in a very good agreement with the ground truth information which is the oncologist‘s contour drawn manually. Key Words: Color Image processing, Edge detection, Trinion, Quaternion, Magnetic Resonance Image.
Holistic Multi-Parametric Magnetic Resonance Image Processing for Identification of Neoplasm and Normal Brain Tissues
(Addis Ababa University, 2016-12) Chernet, Teferi; Assefa, Dawit (PhD.)
Abreakthrough advance in Magnetic Resonance Imaging (MRI) into standard clinical practices has enabled greater accuracy in delineating tumor volumes. The technique allows autonomous detection through noninvasive imaging of malignant brain tumors like gliomas and similar other body tumors for the requirement of efficient and effective procedures like biopsy and radiation therapy as well as for quantification of patients’ responses to treatment. The role of MR imaging in regard of characterization schemes of neoplasms has been tremendous. There are many approaches suggested in the literature for this purpose, one of which is color processing concept. Most color images have three components and each pixel/voxel on such color images can be represented as a three-component vector. Analogously, an MRI voxel, for example, can be seen as a vector with as many channels as available MR image parameters: T1, T2, PD, ADC, rcbv, Ktrans, T2*, etc. And there can be various methods of representing such images as colors. Hence using these methodologies and by combining multiple MRI parameters as we need, we can maximize the information we want to display. The research study exploits advanced mathematical and image analysis assets for use in analysis of MP-MR medical images making use of algorithmic procedures. Accuracy and potentials of the method is compared against gold standards (available ground truth). The data used is composed of standard co-registered multiple MR image parameters taken from patients treated for the highest grade and most aggressive of the gliomas. This approach comes with a great potential to assist accurate and effective detection, visualization, and analysis of useful medical image information for physicians and/or researchers as well. Even though MR images are the subjects of interest in this study, in principle the method developed could be applied to other medical images with multiple components acquired through different modalities. Keywords: Quaternion Fourier Transform, Trinion Fourier Transform, Image Processing, Principal Component Analysis.
Design of Low Cost Temperature Controlled Neonatal Transportation Device
(Addis Ababa University, 2017-01) Liya, Befekadu; Masreshaw, Demelash (PhD)
Temperature instability in preterm neonates is one of the major causes of morbidity and mortality. The immature systems and organs of preterm neonates in combination with poor facilities and after birth care lead to lifelong health complications, if not death. The drop of temperature in preterm neonatesneonate highly increases while they are transported through the embrace of nurses from delivery rooms to Neonatal Intensive Care Units (NICUs) in referral hospitals. They may also be transported by ambulances for large kilometers, because NICUs in Ethiopia are found only in referral hospitals. A significant drop of temperature in the surrounding environment during transportation makes the preterm neonates hypothermic and affects the whole function of their body. Therefore it‟s necessary to measure and optimize the neonates‟ body temperature to the normal level while they are being transported. In order to solve this problem, in this thesis we have designed and prototyped a novel portable device that can be used to transport the neonates in a temperature controlled environment. We believe that this device, once fully developed and implemented, can save thousands of lives of preterm neonates and avoid the pain caused to parents due to lose of their new born babies. The price and simplicity of our design makes it convenient for use in low resource settings and low income countries like Ethiopia. In our design, we have used temperature sensors and microcontroller to measure the body temperature of the preterm neonates and make the right decisions. Heater and fan are used to generate and circulate heat around the preterm neonate. Based on the temperature reading obtained from the temperature sensors, the microcontroller decides whether or not to turn the heater and fan ON. The system uses dis
Design and Development of Advanced External Fixator for Treatment of Femoral Bone Fracture
(Addis Ababa University, 2017-02) Misganaw, Talaksew; Assefa, Dawit (PhD.)
The incidence of accidents causing major injuries globally is increasing every year. Previous studies indicated that more than 90% of the incidences happen in the developing world. In case when the scope of the accident is high, that often creates fractures particularly on strong and heavy bones (femoral and tibia in the lower extremities). In this regard different methods are available to treat fractured bones including tractions, internal and external fixators and their variants. Traction is one of the oldest treatment methods currently used in many clinics as a temporary treatment of long bone fractures through suspension of weights using ropes over pulleys and attached to the patient bed. Such a procedure does not allow patients movement and could result in many known complications: depression, bed sores, pneumonia and urinary infections to mention a few. The internal fixators like intramedullary nails are the gold standard implants for fixation of long bone fractures. However high cost, lack of adequate professionals and instruments in low resource settings, location of the accident, need for multiple surgeries and cultural and/or physiological patient/family related issues for operation significantly hinder patients from getting the service. External fixation management applies aligning/realigning fractures using pins, wires, clamps, and bars or rings. It has advantages of simplicity, ease of adjustability, and increased access for fracture related wound care and wound monitoring. However, the solid bars often used to make fractured fragments rigid has no flexibility, do not employ a force on the muscles tensioned by the fixators and have no micro movement setup. This creates immediate functionality problem after recovery and reduce fast healing process for the fractured bone because loading has considerable effect on tissue repair and remodeling. A thorough investigation of literatures on fixators (especially external fixators) carried out in the current study showed that applying continuous load/force on fractured limbs increases the functionality of the muscles during the bone healing process thereby reducing the rehabilitation time after recovery. In this regard, based on solidwork, an advanced external fixator has been proposed in the current study that makes use of a spring load mechanism to exert a continuous load/force. Detailed 2d and 3d design and simulation of the mechanism has been presented. The proposed external fixator also comes with a traction functionality.
Automatic Detection of Malaria Parasite based on Microscopic Image Analysis
(Addis Ababa University, 2017-02) Bekele, Abebe; Demelash, Masreshaw(PhD)
Automatic Detection of Malaria Parasite based on Microscopic Image Analysis Abebe Bekele Addis Ababa University, 2017 Malaria is a serious global health problem and its diagnosis is usually done manually by compound light microscopy which is time consuming, tiresome and subjective. To support this manual method, in this master thesis, we designed and developed a system which is able to automatically detect plasmodium parasites from images of blood smears acquired by ourselves using a digital light microscope. In this method, blood smears taken from patients who were infected with plasmodium parasites were prepared. Digital images were then acquired by the light microscope and saved in the computer. Red blood cells (RBCs) are first segmented by marker control watershed algorithm, where the foreground markers are obtained from circular Hough transform and background markers from distance transform. The plasmodium infected RBCs are then detected in the Hue-Saturation-Intensity (HSI) color space. Thresholding on hue component of HSI color space is used to detect the chromatin dots of the parasite. Plasmodium falciparum and plasmodium vivax, the two dominant plasmodium species which cause the vast deaths in Ethiopia, are differentiated based on the size of infected RBCs. The performance of the proposed system for RBC segmentation, parasite detection and species differentiations was analyzed by comparing with the gold standard manual method for the total of 91 images of thin blood smears. The result shows that 97% of the RBC counts are similar to the gold standard with 97.5% sensitivity and 84.4% positive predictive value for plasmodium parasite detection at the cellular level. The species differentiations were done for each image with the accuracy level of 91.46%.The result showed the potential of the method for supporting the mass screening of malaria parasite. Keywords: Digital Microscope, Plasmodium, Thin Blood Smears, Watershed Algorithm, Circular Hough Transform, Distance Transform, Hue-Saturation-Intensity (HSI), Thresholding
Modeling Simulating and Quantification of Optical Images at Ultrasound Contrast Bubbles
(Addis Ababa University, 2017-07) Abraham, Habtamu; Assefa, Dawit (PhD.)
Microbubble based ultrasound contrast agents are being used in clinical settings to Enhance backscattered ultrasound signal from blood pool during perfusion and blood flow Measurements. Often individual bubbles are characterized optically by recording their Vibrations with a fast framing camera and direct quantitative information on their dynamic Behavior can be derived. Nonetheless, when a three-dimensional object, stack of infinitely thin Two-dimensional layers, is imaged through a microscope, the image formed onto the charge Coupled device element consists of contributions from all layers. If a bubble is larger than the Depth of focus, the part of the bubble above the focal plane influences the image formation And therefore the bubble size measured. Thus, this thesis presents a methodology to compute Two-dimensional image formation from three-dimensional objects, hollow spheres and find Under which circumstances the optical image formation leads to a significant deviation in Measurement of the actual size. Two-dimension image formations of the three-dimensional Object was computed by convolving the slices of an artificial object, hollow spheres with the Respective weighted point spread function and summing all convolved slices. Finally, image Processing was applied the optical image formed to quantify the object size and a systematic Error was observed for objects in focus with radius 1:65mm. Also it was concluded that Even though a three-dimensional object is in focus, there is discrepancy of up to 0.66% in size Measurement. In addition, size measurement of an object for the same shift above the focus and Below the focus could differ by up to 3.6%. Moreover, defocusing up to 90% could result up To 64.7 mean percentage error. The results reveal that defocusing above 25% severely deviates Size measurements. This thesis hopes to offer a standard for quantification of optical images of Three-dimensional objects, and in the future actual size of an object could be measured from its Defocused optical images.
Stockwell Transform Based Medical Image Compression
(Addis Ababa University, 2017-11) Tewodros, Endale; Dawit, Assefa (PhD); Mengistu, Kifle (PhD) Co-Advisor
Image compression is achieved by reducing the redundant and irrelevant data so that the number of bits to represent each image pixel array is reduced. This could be achieved by using transform coding. Images can be transformed from one domain to a different type of representation using some well-known transform. Different types of linear transforms exist in this regard. The Fourier transform and cosine transform are few examples. Multiresolution analysis is becoming important in image analysis and signal processing as it gives resolution on both frequency and time domains, which is not the case in the Fourier and cosine transforms. Wavelet Transform (WT) is the earlies multiresolution member and was proposed on various image processing fields, JPEG2000 image compression tool uses WT. But due to the lack of the absolutely referenced frequency and phase information and sensitivity to noise the application area of WT is limited. The Stockwell transform (S-Transform) is another proposed multiresolution transform that offers the absolutely-referenced frequency and phase information. There exists a very direct relationship between the S-transform and the natural Fourier transform and the S-transform is always invertible. Such advantages of the S-transform inspired many researchers to navigate very useful applications of the transform in different disciplines. But S-transform redundantly doubles the dimension of the original data set making computation expensive and hence the Discrete Orthonormal Stockwell Transform (DOST) was proposed. DOST is a pared-down version of the fully redundant S-transform which reduces the computational cost without changing its multiresolution nature and the absolutely-referenced frequency and phase information. The proposed image compression scheme in this thesis uses JPEG 2000 image compression scheme as a bench mark. As S-transform outperforms the WT in some useful aspects, WT that is used in the JPEG2000 scheme is replaced by the DOST and a new algorithm has been developed. Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) were used to quantify the performance of the proposed compression scheme. Results showed that the DOST allows effective and efficient image compression and outperforms other transform-based approaches including those that makes use of the discrete cosine as well as wavelet transforms. The proposed algorithm has been tested on images acquired from freely available research database and offered an average PSNR, CR, MSE and SSIM of 47.0099, 50.6031, 8.0097 and 0.9295 respectively. In comparison, WT offered an average PSNR, CR, MSE and SSIM of 42.9151, 49.8519, 8.2633 and 0.9108 respectively.
Brain Tumor Detection Based on Magnetic Resonance Image Analysis
(Addis Ababa University, 2018-01) Amare, Ambaw; Dawit, Assefa (PhD)
Automatic detection of brain tumors based on magnetic resonance (MR) image processing has been developed in this thesis. Improving the ability to accurately identify early-stage tumors is important goal for physicians, because early detection of brain tumors is a key factor in producing successful treatments. In this regard, an automatic brain tumor detection and segmentation framework has been proposed in this thesis work based on contrast enhanced T1 weighted (T1-W) images acquired from a cohort of patients with confirmed high grade brain tumors. Gray scale T1-W images have been represented in the three component Trinion space and Trinion Fourier transform has been applied aiming to extract useful features that could be used to automatically detect and segment brain tumors from their surrounding background. The performance of the proposed scheme has been evaluated by comparing its segmentation outputs with the ground truth information (based on manual contours by radiologists) that came with the MR data set. Results have showed that the algorithm achieved 99.6% sensitivity, 100% specificity, and 99.8% accuracy for pixel based segmentation while it achieved 91.5% sensitivity, 90% specificity and 90.5% accuracy for image based classification of tumors.
Slice Based 3D Cell Segmentation of Optical Projection Tomography Images
(Addis Ababa University, 2018-01) Haile, Baye; Jari, Hyttinen (Prof.)
Optical Projection Tomography (OPT) is a new imaging technique used to image cells in 3D cultured in a hydrogel. Reconstructed OPT images of cells suﬀer from several artifacts. These artifacts reduce the overall image quality. This will create a challenge for isolating and studying each cell in 3D cell culture. It is highly required to enhance the 3D OPT images of cells for successful analysis of cell interaction and growth in 3D cell culture. In that regard, this thesis intends to build a robust algorithm for use in eﬀective segmentation of cells cultured inside hydrogel. There exist various 3D cell segmentation algorithms in the literature including those schemes that rely on thresholding, edge detection, region growing and clustering approaches. Among these algorithms, moving average adaptive thresholding (MAAT) and region growing algorithm present commendable performance in segmentation of cells identiﬁed on OPT images. In this thesis the performance of an automatic seeded region growing algorithm (ASRGA) and MAAT have been compared rigorously in terms of their use in 2D slice based segmentation of the cells on the 3D OPT image sets considered. Results have shown that the MAAT method show superior performance and provide promising 3D visualization of cells. The output of the research will have a tremendous contribution to reduce artifacts in 3D cell images and enhance 3D visualization.
Segmentation of Sonicated Blood Cells and Ultrasound Contrast Agent Microbubbles from Time-Lapse Microscopic Images
(Addis Ababa University, 2018-02-17) Mohammed, Aliy; Dawit, Assefa (PhD)
Blood cells and ultrasound contrast agent microbubbles behave strangely when subjected to an ultrasound ﬁeld. They migrate towards the nodes of the ultrasound wave and form circular patterns. However, differentiation between blood cells and ultrasound contrast agent microbubbles can be easily determined via time-lapse analysis of the pattern formation. Ultrasound contrast agent microbubbles migrate towards the wave nodes very quickly and form tightly packed clusters. In contrast, formation of tightly packed clusters in the case of blood cells is unlikely based on the sonophore model theory of cells. Moreover, the interaction of the microbubbles with the blood cells and the surrounding medium is not fully understood. To study the behavior and interaction of sonicated blood cells and microbubbles on the time-lapse microscopic images, there is a need to deﬁne a contour around the cells and the microbubbles. To do so, ﬁrst, the microbubbles and the cells should be segmented from the rest of image content. In this regard, this thesis devised a scheme that combines features of the Laplacian of the Gaussian detector and a modiﬁed form of the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique for effective analysis of time-lapse microscopic images. The scheme is tested on three datasets (one synthetic and two real) and its subjective and objective performance is found quite pleasing. Absence of ground truth for the real datasets makes the evaluation of the segmentation scheme merely subjective. Objective evaluation is only performed on the computer-simulated time-lapse images. The average segmentation sensitivity, speciﬁcity and accuracy of the proposed algorithm are valued around 0.96, 0.91 and 0.95 on the synthetic dataset out of a unit scale, respectively. The result generated could be a crucial input for effective particle tracking and sizing studies.
Computer Aided Diagnosis System for Melanoma Lesion Detection
(Addis Ababa University, 2018-04) Endalkachew, Wolde; Dawit, Assefa (PhD); Mengistu, Kifle (PhD) Co-Advisor
Detection of skin cancer in the earlier stage is very critical. Nowadays, skin cancer is seen as one of the most hazardous form of cancers found in humans. The most common types of skin cancers are Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC) and Melanoma. Among these melanoma is the deadliest type of skin cancer. The detection of Melanoma cancer in early stage can be helpful to cure it. Computer vision plays an important role in medical image diagnosis and this has been proved by many existing systems. A computer aided skin image diagnosis system has a significant potential for screening and prognosis. It is utmost important in countries where unaided visual diagnosis system is the practice and where there is insufficient number of dermatologists. This thesis presents a computer aided diagnosis system for the detection of melanoma skin cancer using a novel mathematical scheme. The proposed method uses a holistic representation of skin color images to extract useful features for use in effective segmentation of melanoma lesions. The segmentation scheme is preceded by a processing stage composed of noise filtering color space transformation. Vertex component analysis and principal component analysis are integrated to form a hybrid approach for unmixing and feature dimension reduction. An optimized feature selection technique is also used to obtain the best achievable performance in effectively detecting the lesions. Support vector machine (SVM) along with geometrical and color feature threshold values are integrated to make effective detection of melanoma lesions. The effectiveness of the developed scheme to classify lesions into benign, suspicious and malignant melanoma is found to be promising. The proposed scheme has been tested on images taken from standard dermoscopy image databases and achieved 96.4% sensitivity, 99.4% specificity, and 97.9% overall accuracy for pixel based classification of melanomas while it achieved 98% sensitivity, 100% specificity, and 98.6% accuracy for image based classification justifying it‟s great promises.
Automated Breast Cancer Detection using Computer Aided Diagnosis
(Addis Ababa University, 2018-05) Bruhtesfa, Mouhabaw; Dawit, Assefa (PhD)
Breast cancer is the most prevalent invasive cancer in women and stands second for chief cause of cancer deaths in women, next to lung cancer. The occurrence rate is exceeding in the developing countries though the rate of mortality has decreased which can be credited to the advances in diagnosis and treatment. Initial diagnosis involves histological observation (microscopic observation of cells/tissues) of affected breast tissues for structural changes, irregularities in cell shapes, distribution of cells in the tissue and determining the grade of the cancer. As manual interpretation of the tissues is often labor intensive, expensive and prone to errors and inconsistency, computer-based analysis of microscopic histopathology images is used as an alternative to provide a more accurate, automatic, fast and reproducible procedure to assess breast cancers. One important aspect in this regard is the automatic segmentation of breast cancers and several approaches are available in the literature for use in executing such tasks. However, the segmentation of major pathological structures and their subsequent follow– ups are not easy because of various artifacts such as presence of anatomical structures with highly correlated pixels with that of lesion, illumination variability, noise and magnification of the microscope. This thesis attempts to present a new mathematical scheme for analysis of color breast histopathology images acquired through digital microscopy or whole slide imaging. The proposed scheme uses a holistic representation of the color images in the three (trinion) space and applies trinion based Fourier transforms to extract useful imaging features for the purpose of classification and segmentation of histopathology images. A suitable color space transformation and a way of extracting robust higher order features are included in the method. The scheme has been applied in analyzing images acquired from standard histopathology image databases and results have shown that the algorithm achieved commendable results with 91% sensitivity, 92.7% specificity, and 92% overall accuracy.
CT Based Lung Cancer Detection Using Spatially Localized Integral Transforms
(Addis Ababa University, 2018-06) Abel, Belay; Dawit, Assefa (PhD)
Lung cancer is the leading cause of death among other cancer types. Its survival rate is very limited unless diagnosed/detected early. As a result, early detection is imminent for minimizing the deaths caused by lung cancer. For realizing the early detection and early diagnosis, sophisticated and complex imaging modalities like low dose CT are often utilized. However, the big problem lays ahead on the image interpretation. Due to different factors like image quality, suppressed image features in the spatial domain, radiologists eye sight and radiologists’ expertise level, misdiagnosis and error are the major difficulties to overcome. In that regard, many research works have been reported in the literature to deal with the image interpretation issues. Among other researches, CADe and CADx are considered the most remarkable methods for early detection and diagnosis. However, most of the CADe and CADx systems are not clinically implemented. This is because of their inefficiency, inaccuracy and non-robustness. Aiming for a more robust and accurate detection of lung cancers, a new computer aided scheme has been suggested in this thesis. The scheme implements a rotation invariant, joint space-frequency localized integral transform together with spatial image enhancement for feature extraction of CT lung images. The algorithm has been implemented on a Matlab platform (Matlab 2013a) and validated on CT lung images acquired from TCIA database. Results showed that the algorithm achieved a sensitivity of 97.1%, specificity of 83.33% and overall accuracy of 96.68% in detecting lung cancers showing its great promise.
Medical Equipment Supply Chain Management System at PFSA: Warehouse Layout Design and System Software Development
(Addis Ababa University, 2018-06) Zelalem, Lemma; Masreshaw, Demelash (PhD)
Improving the performance of medical equipment supply chain management system is crucial in the healthcare service delivery. The Pharmaceuticals Fund and Supply Agency (PFSA), the responsible body, has strived to systematize the medical equipment supply chain management system (MESCMS) since its establishment despite the fact it needs more improvement. The objective of this study is to investigate, analyze, design and develop an efficient MESCMS that can ensure the quality, safety, reliability of medical equipment through proper delivery, effective distribution, functional supervision and technical support for concerned health facilities (federal and regional hospitals, health center and health post). Qualitative and quantitative methods were used for this case study of MESCMS. Data gathering techniques such as questionnaire, interview, and direct observation were applied for targeted personnel. The participants were selected by purposive sampling technique. In doing so, a total of 54 questionnaires were distributed in PFSA and facilities. From these, 43 questionnaires were filled out and returned back. The survey data analysis result revealed that 88% and 78% of respondents agreed that the storage and distribution practices of medical equipment are not in line with the standard Good Storage Practices (GSP) and Good Distribution Practices (GDP) respectively. Moreover, the data also shows that 83% and 72% of the participants agreed that there is no proper delivery and inspection service of medical equipment at PFSA, respectively. Furthermore, 77.8% and 75.7% of the respondents agreed that PFSA is providing insufficient medical equipment functionality supervision and technical support for facilities, respectively. Finally, 84% of respondents agreed that the PFSA does not have an effective warehouse management system (software) used to keep and make available the required information about medical equipment within the transaction. The regression analysis shows that GDP is dependent variable on independent variables such as Proper delivery, Tracking method, Automation, Storing practices, and Equipment inspection. The model’s degree of explaining the variance in the dependent variable was ��2=0.794. Therefore, the coefficient value tells about 79.4% of the variation in the GDP is explained by all other dependent variables. According to the ANOVA statistics �� = 14.659,�� < 0.05, therefoere, the five independent variables in the standared model are significantly predicative of the dependent variable. PFSA need to have high attention to improve the GDP along with the other significant independent variables. Currently, PFSA is working with computerized management system in order to conduit interaction between facilities and other stakeholders though it is not effective as required. In general, PFSA needs to apply best practices on inspection, storage, distribution, proper delivery, technical support and functionality supervision activities in the management of medical equipment in order to increase customer satisfaction. The lack of proper management of medical equipment has affected the facilities not to deliver adequate healthcare services. Thus, in order to support PFSA to solve the identified problems, the research proposed a novel warehouse layout design and web-based management system. The management system can record and easily retrieve all the relevant information regarding the status of medical equipment both at FSA warehouse and at the facilities after delivery. Such computerized management system not only prevents mix-up in the warehouse and avoids order exchanges while distributing, but also helps trace the status of medical equipment after it leaves the warehouse. It can tell us the facility and the date where and when equipment is installed and commissioned.
Web-based Medical Equipment Management System in Seven Referral Hospitals
(Addis Ababa University, 2018-06) Abiyou, Semegnew; Masreshaw, Demelash (PhD)
Medical devices are an integral part of healthcare service delivery and they are used for monitoring, diagnosis, treatment and rehabilitation of patients. Proper utilization of medical devices is important for ensuring the safety of users and patients, reducing downtime, improving the quality of health services and increasing the benefit of financial investment. The aim of this study was to assess the utilization of medical devices and to develop web-based medical equipment management system. Both quantitative and qualitative data has been collected for the study. The data collection was conducted between August 26, 2017 to September 20, 2017 in all the seven government owned teaching hospitals which are found in Addis Ababa and Hawassa cities. On average, each hospital delivered services for more than 3 million populations, and had more than 17 service standards, 330 medical equipment and 500 beds during the research period. Data were collected by observation of available resources, interviewing selected professionals about their services and also through questionnaire which was filled by biomedical engineers and biomedical technicians who assessed the utilization phase of healthcare technology management in the seven hospitals. According to the data collected, more than half (17/31) of the respondents did not know the exact number of medical equipment in their facility and there were only 29% (2/7) of hospitals which had recommended test equipment and workshop space in their facilities and almost all hospitals did not have international standards and guidelines and also service manuals. The percentage of respondents who didn’t agree on the availability of reference materials, electrical safety and performance test equipment, sufficient user and maintainer training, sufficient maintenance staff, preventive maintenance schedule for all medical equipment were 74%, 77%, 65%, 54% and 36% respectively. In order to better manage medical devices, we have designed and developed an automated medical equipment management system that can be deployed in a hospital. The system is developed using HTML5, CSS3, JavaScript, PHP, and MySQL. We strongly believe that such automated system helps to improve communication between system users, decrease resilience on human role, reduce data redundancy, allow multiple users to access the data, enhance the security of the equipment, improve planning and budgeting for medical equipment.
Evaluation of Energy Potential from Solid Medical Waste at Tikur Anbessa Specialized Hospital
(Addis Ababa University, 2018-09) Azeb, Tayework; Wondwossen, Bogale (PhD); Masreshaw, Demelash (PhD)
The waste-to-energy conversion processes are expected to play an increasing important role in sustainable management of solid waste. The primary function of waste-to-energy technology has been volume reduction and power production. Waste management has become a critical issue as solid waste possesses potential health risk and damage to the environment. Management of solid medical waste has not yet got the priority it deserves. Researches indicated that in the past solid waste has done much damage to the environment and to public health. As we know Tikur Anbessa Specialized Hospital is the biggest hospital in Ethiopia. But the managing of solid medical waste is poor, even practice of incineration is very old and it is not environment friendly. The general objective of this study is to assess waste management practices and to evaluate the amount of energy that can be generated from solid medical waste at Tikur Anbessa Specialized Hospital. The methodologies are descriptive and consist of the use of survey, in depth interviews, meeting, discussion and participant observed strategy with the concerned body of the hospital. It also involved in the identification and characterization (type and laboratories work) of waste type. After characterization Thermoflex software was used to estimate the power output. The result shows that the average solid medical waste generation rate was determined 1396.41 kg/day from this about 68.26 % was food waste, 14.68 % was plastic waste, 12.84 % was paper waste, 2.99 % was wood waste and 1.23% was cloth waste. The annual estimated waste generation was 507,923.57 kg or 507.9 tons. In general solid medical waste management was found in poor manner. The gross and net electric power output for a mass flow rate of 0.217 kg/s was 0.84MW and 0.731MW respectively. Based on the result, the net electric efficiency of the system was 18.96%. Based on the result the system was affected by ash content, moisture content, excess air and lower heating value here of optimizing input parameter then was increased power output from 840.6 kW to 891.3kW or 0.84MW to 0.89MW. To optimize the power output we can reduce boiler exit temperature and condenser pressure. And also minimizing of ash content and moisture content are improving the output.
An Automated System Design for Medical Image Storage and Distribution
(Addis Ababa University, 2018-11) Abdela, Kemal; Dawit, Assefa (PhD)
The advent of information and communication technologies (ICT) and their incorporation into the medical domain especially medical imaging have created opportunities to enhance medical services and provide improvement to patient care. To implement such services, the current medical system needs to be integrated to different imaging modalities and at the same time be available to health professionals and patients. Picture Archiving and Communication System (PACS) is one means of storing and uses a server for image transmission through a network. The images could be acquired using any given modalities like Computed Tomography (CT), Ultra Sound (US) or Magnetic Resonance Imaging (MRI) and stored digitally. Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL 7) are often standards used to exchange medical images and patient data within the health institutions and outside of the institution using public network (internet). The images taken from the different imaging modalities are often difficult to transfer over the internet because of their size. We could think of for example storing and transferring an isotropic MRI data set between systems even for few patients. In order to transmit such slices of pictures via the internet, these images need to be compressed first. One of the primary obstacles in the development of a compression scheme is the loss of image resolution and contrast. However, we can improve the transmission time and also upload and download time. In the existing setup of the health facilities in Ethiopia, working medical image sharing systems are available only is some hospitals and function only within the hospitals. In low resource settings with chronic shortage of medical experts to examine the ever increasing amount of imaging data, there is always a need to locate a physician to consult and hence having a system that allows two ways communication remotely could be an enormous benefit. This thesis focuses on the development of a working Medical Image File Storage and Distribution System (MIFSDS) for use in storage and exchange/sharing of medical images within and across hospitals. The system includes development of an image classification algorithm and design of a web page system for image distribution and communication. The image classification algorithm which has been developed based on higher order statistical image feature extraction is shown to be accurate and robust while the system that has been developed for easy storage and transmission of imaging data allowing two way communication is proved to be simple and effective.

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