AcknowledgementsIn the beginning, we would like to thank the almighty Allah for enabling us to conduct our research and allowing us to successfully conclude it.Moreover, we would like to express our gratitude and appreciation to all those outstanding person who collaborated us throughout this research period. First of all, special thanks are due to Assistant Professor Dr. Md. Ashraful Alam, our thesis supervisor. He gave us invaluable advice, provided insight and helped us to implement this research work by providing helpful instructions about how to proceed with our work. In times of severe difficulties, he inspired us well and helped us to overcome the situation.Last but not least, we are thankful to the faculties, seniors, friends and our beloved family who have motivated and inspired us throughout this journey. We should also appreciate all sorts of knowledge we could acquire from various resources from the internet from the work of fellow scholars and researchers.ii | P a g e Abstract Advancements in computing speed and power have made revolutionary changes in medical science practices and this is no different for cardiology. Such advancements in computer sciences have made the existing medical tests of heart into being. These tests are: ECG, CTA, & Echocardiogram. CTA (Computed Tomography Angiography) is a widely used imaging technique to visualize arterial and venous vessels throughout the body. In clinical practice, the analysis mainly relies on visual inspection or manual measurements by experienced cardiologists. The proposed method aims towards a full automation of the detection of coronary artery blockage through some image processing techniques so that the system does not have to rely on any human's inspection. The goal of the research is to implement the proposed image processing techniques so that the system can detect the narrowing area of the wall of coronary arteries due to the condensation of different artery blocking agents. This detection is crucial for further analysis of the heart. The research suggests that the system will require a 64-slice CTA image as input. After the acquisition of the desired input image, it will go through several steps to determine the region of interest. This research proposes a two stage approach that includes the pre-processing stage and decision stage. The pre-processing stage involves common image processing strategies while the decision stage involves the extraction and calculation of two feature ratios to finally determine the intended result. In order to get more insights of the subject of these examinations, this research has proposed the use of an algorithm to create a 3-D model. Moreover, the system to work more precisely and effectively, use of several techniques have been suggested including parallel processing with shared memory allocation between the CPU and the GPU. Using the parallel processing technique not only makes the whole process at least 7 times faster, but also helps several stages of the process work more effectively.iii | P a g e

No abstract

Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates beautiful images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.