Liver Segmentation in Medical Imaging Using MATLAB with Algorithm Implementation

Liver segmentation represents a critical procedure in medical image analysis that focuses on precisely delineating hepatic tissues from surrounding organs and anatomical structures. This process serves fundamental roles in clinical diagnostics, therapeutic planning, and hepatic function assessment. Both manual annotation and automated computational methods are employed for liver segmentation, with recent advancements heavily favoring machine learning approaches. Deep learning architectures, particularly convolutional neural networks (CNNs), have demonstrated remarkable efficacy in hepatic tissue identification through automated feature extraction and boundary detection. In MATLAB implementations, liver segmentation typically involves several key steps: image preprocessing using functions like imadjust() for contrast enhancement, region-growing algorithms with regionprops() for initial segmentation, and active contour methods implemented via activecontour() for boundary refinement. Deep learning approaches leverage MATLAB's Deep Learning Toolbox, where U-Net architectures can be constructed using layerGraph() and trained with trainNetwork() on annotated CT datasets. A recent study achieved a Dice similarity coefficient of 0.95 using a customized CNN implementation with optimization through trainingOptions() and performance validation via dice() metric calculations. The implementation workflow generally follows: 1) Data loading and preprocessing with imread() and imresize(), 2) Feature extraction using edge() or gradient-based methods, 3) Mask generation through thresholding with graythresh() or adaptive methods, 4) Post-processing using morphological operations like imopen() and imclose() for smoothing. For deep learning implementations, the process includes patch extraction using blockproc(), network training with compute-intensive layers like convolution2dLayer(), and prediction validation through semantic segmentation evaluation metrics. MATLAB's comprehensive image processing toolkit provides robust solutions for liver segmentation challenges, offering specialized functions for 3D volume processing (volshow()), histogram analysis (imhist()), and interactive segmentation tools (imageSegmenter app). The integration of GPU acceleration through gpuArray() significantly enhances computational efficiency for deep learning models processing high-resolution medical images. These technical capabilities make MATLAB particularly suitable for developing and validating liver segmentation algorithms that require high precision and clinical reliability.