The purpose of this paper is to provide an in-depth analysis of computer aided system for the early diagnosis of Deep Vein Thrombosis (DVT). Normally, patients are diagnosed with DVT through ultrasound examination after they have a serious complication. Thus, this study proposes a new approach to reduce the risk of recurrent DVT by tracking the venous valve movement behaviour. Inspired by image processing technology, several image processing methods namely, image enhancement, segmentation and morphological have been implemented to improve the image quality for further tracking procedure. In segmentation, Otsu thresholding provides a significant result in segmenting valve structure. Subsequently, morphological dilation method is able to enhance the region shape of the valve distinctly and precisely. Lastly, image subtraction method is presented and evaluated to track the valve movement. Based on the experimental results the normal range of valve velocity lies within the range of blood flow velocity (Vb) and occasionally may result in higher values.
Identification of Dendritic Cell (DC) particularly in the cancer microenvironment is a unique disclosure since fighting tumor from the harnessing immune system has been a novel treatment under investigation. Nowadays, the staining procedure in sorting DC can affect their viability. In this paper, a computer aided system is proposed for automatic classification of DC in peripheral blood mononuclear cell (PBMC) images. Initially, the images undergo a few steps in preprocessing to remove uneven illumination and artifacts around the cells. In segmentation, morphological operators and Canny edge are implemented to isolate the cell shapes and extract the contours. Following that, information from the contours are extracted based on Fourier descriptors, derived from one dimensional (1D) shape signatures. Eventually, cells are classified as DC by comparing template matching (TM) of established template and target images. The results show that the proposed scheme is reliable and effective to recognize DC.
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