Stony Brook University 3 credits AMS
This course explores the fundamental principles and algorithms for medical image processing and analysis, covering both traditional and machine learning techniques. Key topics include image interpolation, registration, enhancement, feature extraction, segmentation, classification, quantification, shape analysis, motion estimation, and visualization. We will examine data from common anatomical and functional imaging modalities (e.g., X-ray, CT, MRI, PET, Ultrasound). Through programming assignments and a course project using tools like Python, NumPy, and deep learning frameworks, students will gain practical skills analyzing real-world clinical datasets. This course is designed for students in biomedical engineering, computer science, applied mathematics, or related fields with an interest in medical imaging applications. 3 credits Grading Letter graded (A, A-, B+, etc.)
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