An Approach Towards Effective Processing of Liver Tumour Data

Liver tumour has become one of the life-threatening problems which need to be effectively diagnosed. Accurate detection and segmentation of tumour region is crucial for diagnosis. The major challenge persists with existing procedures lack on false positive values. The objective of the work is to preprocess the Liver CT images effectively in order to derive the images capable of detecting the tumour infected area effectively. The proposed novel work focused on creating an automated liver CT image analysis through image processing technique. The need for effective preprocessing is evaluated here. The study considers MICCAI image dataset for evaluation to have a robust preprocessing framework derived through Boundary extraction algorithm (BEA). The purpose is to extract the region of interest accurately through contour segmentation in order to differentiate the liver structure from the background tissues. To extract the required image intensities highlighted into the extracted region, multi-scaling wavelet transform is applied. The primary challenge of the system is to detect the abnormal pixels present in the input CT images under test, accurately and rapidly. The proposed novel Consistency aware natural looking histogram equalization (CANHE) model is developed to enhance the quality of the input image under test. The presented system comprehensively evaluated to interpret the development of performance measure while integrating the proposed CANHE module and multi-scaling wavelet transform for feature extraction.