MATLAB Implementation for Medical Image Processing with Segmentation Algorithms

In medical applications, image processing plays a critical role in assisting physicians to better understand patient conditions. One essential aspect is image segmentation, which involves separating different regions within medical images. Two common implementation methods for image segmentation include region growing-based approach and optimal threshold-based segmentation. The region growing method performs segmentation by starting from seed points and iteratively adding neighboring pixels that satisfy predefined similarity criteria. In MATLAB implementation, this typically involves functions like regiongrow or custom algorithms using pixel intensity comparisons and connectivity checks. The algorithm requires careful selection of seed points and similarity thresholds to prevent over-segmentation or under-segmentation. Optimal threshold-based segmentation identifies the ideal threshold value that best separates different regions based on intensity distribution. MATLAB implementations often utilize Otsu's method (using graythresh function) or maximum entropy thresholding to automatically determine the optimal threshold. This method converts grayscale images into binary images using imbinarize function with the calculated threshold, effectively partitioning the image into distinct regions. Both methods have their advantages and limitations. Region growing excels in handling heterogeneous regions but requires manual seed selection, while threshold-based methods work well with bimodal histograms but may struggle with complex intensity distributions. Medical professionals and image processing specialists must select the most appropriate method based on specific clinical requirements, image characteristics, and desired segmentation accuracy. MATLAB provides comprehensive toolboxes and functions to implement and compare these segmentation techniques efficiently.