Location detection of key areas in medical images based on Haar-like fusion contour feature learning

Abstract

BACKGROUND: Key area location is an important content of medical image processing and an important detail of auxiliary medical diagnosis. OBJECTIVE: In this paper, a prior knowledge fusion method based on Haar-like feature and contour feature is proposed to locate and detect key areas in medical images. METHOD: For the image to be processed, six Haar-like features and five contour features are extracted respectively. The improvement of Haar-like feature extraction template better adapts to the complexity of regional structure of medical images. The design of the contour feature extraction process fully reflects the consideration of feature invariance. The two features, together with prior knowledge, are fed into their respective decision makers and final fusers as the basis for determining and locating key regions. RESULTS: The experimental results show that the proposed method has excellent performance in locating key regions of medical images on MRI. When the capacity of the database increases from 10 to 200, the accuracy of locating the key areas of the image to be processed still reaches more than 90%. CONCLUSION: The proposed method realizes the accurate location of the key areas of medical images, which is of great significance for the auxiliary medical diagnosis.

Radiomics and Its Feature Selection: A Review

“…Computer-aided inspection systems commonly employ a variety of segmentation techniques, including active contour, level-set, area-based, and graph-based methods [85,86]. Parametric techniques, such as active contour and level-set models, require appropriate initialization and may have limitations when handling uncertain shapes or unknown ROIs locations.…”