Computer Assisted Cytological Medical Image Analysis

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The proposed research leverages advances in Pattern Recognition, Image*Processing, Computational Geometry and Machine Learning to develop novel*solutions to address problems in the area of Medical Imaging. Specifically, I*am furthering my research on Computer Aided Medical Diagnosis for cytological*imagery of breast cancer fine needle biopsies, and of thin blood smears for*malaria detection. For these types of cytological images, my long term*objective is to develop robust, accessible, rapid, and accurate Medical*Diagnosis tools to provide an automatic independent second opinion, to help*avoid errors/oversights, to Pathologists, Clinicians and Medical Specialists,*or act as an expert system to aid the non-specialist medical practitioner.**The process of computer-assisted medical diagnosis or malignancy grading can*be described in two main stages: feature extraction from medical imagery and*classification of the imagery with respect to specific medical issues. One*objective of the proposed work is the determination of improved feature sets*for specific types of cytological images. Many of the frequently used*features used for medical image classification problems require accurate*image segmentation prior to determining these features. Therefore, I am*studying approaches to improve the accuracy and speed of the segmentation of*cells in cytological images by extending my previous work ranging from the*use of the Hough transform to using active contours models, to incorporate*effective use of texture, per-pixel classifiers, and shape modeling. We are*also extending this research to the accurate segmentation of new medical*imaging technologies such as very large full slide virtual images, 3D images,*and images created with extended focal imaging (EFI).**Another objective of the proposed research is to improve the accuracy of the*malignancy classification in cytological images of breast cancer fine needle*biopsies. Specifically, we study the problems of malignancy diagnosis to*determine whether the slide is malignant or benign, and of malignancy grading*where we use the Bloom-Richardson malignancy grading. To accomplish this,*with optimized feature sets, we will develop systems of classifiers that*improve sensitivity rates, particularly with minimal rates of false*negatives. Since the classifiers have to work in a clinical setting, we will*adapt the classifiers to do automatic parameter tuning. The third objective*of the proposed research is to improve the detection of malaria parasites in*cytological images of blood smear slides. Building on our previous complete*blood count framework, we will develop a computer-assisted malaria detection*system to determine the presence of malaria and return accurate infection*counts per RBC counts in thick blood smears; and to differentiate between the*four species of the malarial parasite in thin blood smears. We will develop*systems of classifiers with improved sensitivity rates.**My work in Health Informatics contributes to the larger trend in medical*imagery to develop computer assisted medical diagnosis tools to complement*and facilitate the work of Clinicians and Pathologists. All developments are*designed to use commonly available microscopy and computing resources. For*example, in the rural areas of malaria stricken countries there are*frequently no specialists to do microscopic screening of blood slides for*malaria. Through the automatic analysis of these slides, the computer*assisted expert systems proposed in my research would help fill the knowledge*gap between a specialist with the necessary expertise and the practitioner*actually examining cytological slides, thus leading to better patient care.