Enhancement of Single and Composite Images Based on Contourlet Transform Approach

Image enhancement is an imperative step in almost every image processing algorithms. Numerous image enhancement algorithms have been developed for gray scale images despite their absence in many applications lately. This thesis proposes hew image enhancement techniques of 8-bit single and composite digital color images. Recently, it has become evident that wavelet transforms are not necessarily best suited for images. Therefore, the enhancement approaches are based on a new 'true' two-dimensional transform called contourlet transform. The proposed enhancement techniques discussed in this thesis are developed based on the understanding of the working mechanisms of the new multiresolution property of contourlet transform. This research also investigates the effects of using different color space representations for color image enhancement applications. Based on this investigation an optimal color space is selected for both single image and composite image enhancement approaches. The objective evaluation steps show that the new method of enhancement not only superior to the commonly used transformation method (e.g. wavelet transform) but also to various spatial models (e.g. histogram equalizations). The results found are encouraging and the enhancement algorithms have proved to be more robust and reliable

Enhancement of Single and Composite Images Based on Contourlet Transform Approach

Image enhancement is an imperative step in almost every image processing algorithms. Numerous image enhancement algorithms have been developed for gray scale images despite their absence in many applications lately. This thesis proposes hew image enhancement techniques of 8-bit single and composite digital color images. Recently, it has become evident that wavelet transforms are not necessarily best suited for images. Therefore, the enhancement approaches are based on a new 'true' two-dimensional transform called contourlet transform. The proposed enhancement techniques discussed in this thesis are developed based on the understanding of the working mechanisms of the new multiresolution property of contourlet transform. This research also investigates the effects of using different color space representations for color image enhancement applications. Based on this investigation an optimal color space is selected for both single image and composite image enhancement approaches. The objective evaluation steps show that the new method of enhancement not only superior to the commonly used transformation method (e.g. wavelet transform) but also to various spatial models (e.g. histogram equalizations). The results found are encouraging and the enhancement algorithms have proved to be more robust and reliable

Automated Rheumatic Heart Disease Detection from Phonocardiogram in Cardiology Ward

Rheumatic Heart Disease (RHD) is a preventable and treatable form of cardiovascular diseases. It is also re- ferred to as the ailment of the disadvantaged mainly affecting children and young adults. RHD is recognized as a global health priority by World Health Organization. This chronic heart condition silently deteriorates the normal function of the heart valves which can be detected as a heart murmur using a stethoscope. As the cardiac auscultation process is an elusive process, the clinician will always be tempted to refer the patient for expensive and sophisticated imaging procedures like echocardiography. In this study, a machine learning algorithm is developed to augment the limitation in the auscultation process and transform the stethoscope as a powerful screening tool. For this current study, an RHD heart sound data set is recorded from one hundred seventy subjects. A total of twenty-six features are extracted to model murmur due to RHD. Twenty-four classification and regression algorithms have been tested out of which the Cubic SVM has demonstrated su- periority with a classification accuracy of 97.1%, with 98% sensitivity, 95.3 % of specificity 97.6% precision. The corresponding positive predictive values (PPV) are 96% and 97% for normal and RHD respectively. The results are based on data collected from a cardiology ward where there are more pathological cases than con- trols. Hence it is a valuable detection tool in a cardiology clinic. But in the future, integrating this machine learning algorithm with a mobile phone can be a powerful screening tool in places where access to echocardiography and cardiologist is difficult. Thus, it can then aid a timely, affordable and reliable detection tool allowing a non-medically trained individual to screen and detect RHD.status: publishe

Prevalence of rheumatic heart disease in a major referral cardiology clinic in Ethiopia: A retrospective cross-sectional study.

BackgroundRheumatic Heart Disease (RHD) remains one of the major causes of death and disability in developing countries. This preventable, treatable but not curable form of cardiovascular disease is needlessly killing scores of children and youth mainly due to the misunderstanding of the burden of the disease in these countries. We sought to describe the prevalence of RHD at one of the major referral cardiology clinics in Ethiopia.MethodsThis was a retrospective cross-sectional chart review of all patients referred for a cardiopathy at the Tikur Anbessa Referral Cardiac Clinic from June 2015 to August 2018. We excluded records of patients with a non-cardiac diagnosis and those without a clear diagnosis. A predesigned and tested EXCEL form was used to collect the data. The data was encoded directly from the patient record files. MATLAB's statistics toolbox (MATLAB2019b) was used for statistical analysis.ResultsAmong the total 7576 records analyzed 59.5% of the patients were women. 83.1% of the data belonged to adult patients with the largest concentration reported in the 18 to 27 age group. 69.7% of the patients were from urban areas. The median age of the study population was 30 (interquartile range = 21-50). 4151 cases were caused by RHD which showed that RHD constituted 54.8% of the cases. The median age for RHD patients was 25 (interquartile range = 19-34). The second most prevalent disease was hypertensive heart disease which constituted 13.6% that was followed by congenital heart disease with 9% prevalence rate.ConclusionThe results of this study indicated the extent of the RHD prevalence in Ethiopia's cardiac hospital was 54.8%. What was more critical was that almost 70% of the RHD patients were mainly the working-age group(19 to 34 years)