Artificial intelligence optimized image segmentation techniques for renal cyst detection

The vast number of image modalities available nowadays has given rise and access to a number of medical images. These images perhaps suffer issues such as low contrast, noise, ill-defined boundaries and poor visualisation. Therefore, a need for effective segmentation arises. Medical image segmentation plays a significant role in identifying a disorder, treatment planning, routine follow ups and computer-guided surgery respectively. The paper presents automatic medical image segmentation to overcome the imaging concerns and demarcate each notch & boundary in an image. The proposed algorithm identifies the existing kidney cyst precisely as they may be related to extreme disorders that may affect kidney function. The algorithm has been further tested on automatic segmentation using Genetic Algorithm, Ant Colony Optimisation and Fuzzy C Means Clustering. In terms of visualisation of valuable pathology, GA stands out and further helps in better assessment of the extent of the disease providing with better representation of the kidney cysts thereby giving a better diagnostic assurance and understanding of the nature of any disorder helping the medical practitioners as well as the patients. Experimental results on segmentation of kidney CT images conclusively demonstrate that the Genetic Algorithm is much more effective and robust.