A novel technique for analysing histogram equalized medical images using superpixels

Figures & data

Figure 1. Original image, global histogram equalized image, CLAHE equalized image, and their respective histograms. (a) Original Image (b) Histogram of Original Image. (c) Global Histogram Image (d) Histogram of GHE Image. (e) CLAHE Image (f) Histogram of CLAHE Image.

Figure 2. Flowchart of the proposed method. (a) Segmentation of Original Image. (b) Segmentation of GHE Image. (c) Segmenatation of CLAHE Image.

Figure 3. Comparison of segmentation results using four superpixel segmentation algorithms (a) superpixels of the original image (b) shows the superpixels generated for GHE image and (c) shows the superpixel segments of CLAHE image. (a) Input image (b)superpixel segmentation (c) GHE (d) CLAHE.

Table 1. Comparison of superpixels.

Before HE	SLIC	Quick shift	Felzenszwalb	Watershed
No. of Superpixels	100	163	195	100
Compactness	Non-uniform	Non-uniform	Non-uniform	Non-uniform
Parameters	1	2	1	2
After GHE	SLIC	Quick shift	Felzenszwalb	Watershed
No. of Superpixels	100	186	219	100
Compactness	Uniform	Non-uniform	Non-uniform	Non-uniform
Parameters	1	2	1	2
After CLAHE	SLIC	Quick shift	Felzenszwalb	Watershed
No. of Superpixels	100	190	266	100
Compactness	Uniform	Non-uniform	Non-uniform	Non-uniform
Parameters	1	2	1	2

Figure 4. Some test images: An input image (column (a)), its superpixel segmentation by using four segmentation algorithms (b). Our work is focused on generating superpixel segments of GHE image (c), of CLAHE image (d) and compare their results.

Table 2. Some publicly available datasets used for performance evaluation of the proposed method.

Name of dataset	Download link
BrainWeb	http://brainweb.bic.mni.mcgill.ca
Harvard medical school website	http://www.med.harvard.edu
Open Access Biomedical ImageSearch Engine	https://openi.nlm.nih.gov