Two Phased Methodology for Multiorgan Detection in Abdomen CT Images

Multi-organ detection is important in medical vision field, particularly in identifying diseases and planning therapies. However, challenges such as low contrast, noise, and overlapping structures in CT images makes this task difficult. This paper focuses on addressing these challenges with a Two-Phased for Multi-Organ Detection Method (TPMMD) that proposes Efficient Contrast Limited Adaptive Histogram Equalization Weighted Grey Wolf Optimization Algorithm (ECLAHE-WGWO) to enhance the CT image and X-DenseNet for precise segmentation of liver, spleen and aorta organs. The proposed technique is tested on BTCV dataset and achieves high performance with Dice scores of 96.9% for the liver, 94.23% for the aorta, and 97.73% for the spleen. These results of TPMMD show that the proposed technique outperforms those of cutting-edge techniques.