OPBS-SSHC: outline preservation based segmentation and search based hybrid classification techniques for liver tumor detection

Cancer in Liver is the one among all other types of cancer which causes death of carcinogenic victim people throughout the world. GLOBOCAN12 was an initiative for simultaneously generating the expected dominance and mortality incidence that raised out of the cancer over the whole globe. It reported that about 782,000 new cases in the population were reported to have liver cancer, in which around 745,000 people loosed their lives from these kind of diseases worldwide. Some traditional algorithms were found to be widely used in liver segmentation processes. However, it had some limitations such as less effective outcomes in terms of proceeded segmentation operations and also it was very difficult to apply tumor segmentation especially for larger severity intensities of tumor region, which usually gave rise to high computational cost. It was also required to improve the performance of those algorithms for diagnosing even the tiniest parts of liver along with the improvisation needed when there was misclassification of the tumors near the liver boundaries. Along this way as an improvising methodology, an efficient method is proposed in order to overcome all the above discussed issues one by one through our work. The novelty/major contribution of this proposed method is being contributed in three stages namely, preprocessing, segmentation and classification. In preprocessing, the noises of image will be removed and then, the input image edge will be sharpened by using a frequency-based edge sharpening technique which aids in taking the pixels in the images into consideration for proceeding with the next operation of segmentation. The segmentation process gets the appropriated preprocessed images as input and the Outline Preservation Based Segmentation (OPBS) algorithm is used to segment the images in the segmentation phase. The algorithms involving features extraction were preferably deployed to extract the corresponding features from an image. So, the features present in the segmented image serves as the necessary information for the classification purposes. Next, the features were classified in the classification phase by using novel similarity search based hybrid classification technique. The Outline Preservation Based Segmentation and Search Based Hybrid Classification (OPBS-SSHC) used the 3D IR CAD dataset. It was used to analyze with various parameters such as accuracy, precision, recall, and F-measures. Volumetric Overlap Error (VOE), Jaccard, Dice, and Kappa will be determined later on to predict the errors in the segmentation process undertaken. The proposed method of OPBS-SSHC performance was found to be better than other classification techniques of Relevance Vector Machine (RVM), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM), which were considered for comparison by taking the above metrics and coefficients as and when required throughout this extensive comparative study.