Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images

• Main Authors: Gandhamal, A., Talbar, S., Gajre, S., Hani, A.F.M., Kumar, D.
• Format: Article
• Published: Elsevier Ltd 2017
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014474714&doi=10.1016%2fj.compbiomed.2017.03.001&partnerID=40&md5=c6622770999f5a0fd6ebbf03efc3979b; http://eprints.utp.edu.my/19559/

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images. © 2017 Elsevier Ltd