A novel technique for analysing histogram equalized medical images using superpixels

• Main Authors: Li Yao, Sohail Muhammad
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2019-10-01
• Series: Computer Assisted Surgery
• Subjects: SLIC; Superpixel; Quick shift; Watersheds; Felzenszwalb; AHE; CLAHE
• Online Access: http://dx.doi.org/10.1080/24699322.2018.1560100

We present a novel technique to distinguish between an original image and its histogram equalized version. Histogram equalization and superpixel segmentation such as SLIC (simple linear iterative clustering) are very popular image processing tools. Based on these two concepts, we introduce a method for finding whether an image (grayscale) is histogram equalized or not. Because sometimes we see images that look visually similar but they are actually processed or changed by some image enhancement process such as histogram equalization. We can merely infer whether the image is dark, bright or has a small dynamic range. Moreover, we also compare the result of SLIC superpixels with three other superpixel segmentation algorithms namely, quick shift, watersheds, and Felzenszwalb’s segmentation algorithm