A fuzzy and spline based dynamic histogram equalization for contrast enhancement of brain images

Abstract

In general, medical images acquired at insufficient lighting conditions are suffering from low contrast issues which are inadequate for image analysis steps. One standard solution to improve the image contrast is to change its intensity distribution with the help of an image histogram. The histogram‐based contrast enhancement methods treat the images as regions rather than objects which would be more useful for applications like brain image enhancement. In this paper, a Fuzzy and Spline based Histogram Equalization (FSDHE) is proposed to perform contrast enhancement with medical images. The proposed FSDHE method partitions the image into connected components, and the type of components are identified with a fuzzy membership function. The dynamic histogram equalization is applied to each component individually. The equalized sub‐histograms are combined to drive the global histogram, which is inconsistent as dynamic histogram equalization treated the intensity range for each connected component differently. Hence, a spline‐based histogram smoothing is proposed here in this research work. The equalized intensity mapping is received as control points for the polynomial curve, and a smooth intensity transformation is interpolated as a spline curve. The proposed FSDHE model is analyzed with MRI‐brain image dataset of 3064 images, which consists of both benign and malignant cases. The contrast enhancement performance of the proposed FSDHE method is quantified by various measures like Absolute Mean Brightness Error (AMBE), Peak Signal to Noise Ratio (PSNR), Contrast (C), Weber Contrast (WC), Entropy, Hausdorff Distance (HD) and Texture Preservation (TP) measures. The performance of the FSDHE method is compared against with other histogram equalization methods, and the results indicate that the FSDHE method achieves better quality measures of 3.1401, 30.5499, 21.5486, 0.7779, 4.0252, 0.2777, and 0.7836 for the seven‐performance metrics.

MRI Image Enhancement Based Fuzzy C-Mean Segment and Modified Adapted Histogram Equalization

“…Future studies can employ the suggested technique to enhance CT images. RF [18], (d) DIE [19], (e) FLSMF [14], (f) FSBDHE [15], (g) MCHE [16], and (h) Sug…”

“…Figure . 8 MRI brain Images 37 enhanced by using several algorithms: (a) original , (b) LFE [17], (c) RF [18], (d) DIE [19], (e) FLSMF [14], (f) FSBDHE [15], (g) MCHE [16], and (h) Sug [17], (c) RF [18], (d) DIE [19], (e) FLSMF [14], (f) FSBDHE [15], (g) MCHE [16], and (h) Sug…”

“…NIQE is a completely blind quality analyser that focuses entirely on quantifiable derivations from the statistical regularities observed in real-world images, without any exposure to or training on distorted [17], (c) RF [18], (d) DIE [19], (e) FLSMF [14], (f) FSBDHE [15], (g) MCHE [16], and (h) Sug images that have been graded by humans. Based on a straightforward and efficient space domain natural scene statistic algorithm, it builds a quality-aware collection of static features.…”

“…This suggested algorithm achieves better quality assessment of coloured images than other enhancement method, But the whiteness is high in the enhanced images .The fuzzy-based histogram equation was proposed by S. Saravanan and R. Karthigaivel. [15] to implement medical image contrast enhancement. The proposed method divides the image into connected regions according to a fuzzy membership function and spline-based dynamic histogram equalization (FSBDHE).…”

“…Figure 9. The MRI brain Images 46 enhanced by using several algorithms: (a) original, (b) LFE[17], (c) RF[18], (d) DIE[19], (e) FLSMF[14], (f) FSBDHE[15], (g) MCHE[16], and (h) Sug…”