New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection

Abstract

Multiple sclerosis (MS) is one of the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques. The damage of myelin sheath alters the normal transmission of nerve flow at the plaques level, consequently, a loss of communication between the brain and other organs. The consequence of this poor transmission of nerve impulses is the occurrence of various neurological symptoms. MS lesions cause mobility, vision, cognitive, and memory disorders. Indeed, early detection of lesions provides an accurate MS diagnosis. Consequently, and with the adequate treatment, clinicians will be able to deal effectively with the disease and reduce the number of relapses. Therefore, the use of magnetic resonance imaging (MRI) is primordial which is proven as the relevant imaging tool for early diagnosis of MS patients. But, low contrast MRI images can hide important objects in the image such lesions. In this paper, we propose a new automated contrast enhancement (CE) method to ameliorate the low contrast of MRI images for a better enhancement of MS lesions. This step is very important as it helps radiologists in confirming their diagnosis. The developed algorithm called BDS is based on Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) and Singular Value Decomposition with Discrete Wavelet Transform (SVD-DWT) techniques. BDS is dedicated to improve the low quality of MRI images with preservation of the brightness level and the edge details from degradation and without added artifacts or noise. These features are essential in CE approaches for a better lesion recognition. A modified version of BDS called MBDS is also implemented in the second part of this paper wherein we have proposed a new method for computing the correction factor. Indeed, with the use of the new correction factor, the entropy has been increased and the contrast is greatly enhanced. MBDS is specially dedicated for very low contrast MRI images. The experimental results proved the effectiveness of developed methods in improving low contrast of MRI images with preservation of brightness level and edge information. Moreover, performances of both proposed BDS and MBDS algorithms exceeded conventional CE methods.

Keywords: BPDFHE; Brightness preservation; Contrast enhancement; Lesion detection; MRI; MS; SVD-DWT.

Fig. 1

General process of the proposed method BDS for contrast enhancement of low contrast MRI images

Fig. 2

a Input image and b corresponding LL, LH, HL, and HH sub-band images

Fig. 3

Flowchart of the proposed method MBDS

Fig. 4

T1-weighted MRI image of the brain (sagittal section). a Original image and b–g enhanced images with DWT-SVD, AGC, GCDWT-SVD, MBBDHE, BDS, and MBDS respectively

Fig. 5

h1–h7 The corresponding histograms for Fig. 4

Fig. 6

T1-weighted MRI image of the brain (axial section). a Original image and b–g enhanced images with DWT-SVD, AGC, GCDWT-SVD, MBBDHE, BDS, and MBDS respectively

Fig. 7

h1–h7 The corresponding histograms for Fig. 6

Fig. 8

Original and enhanced images with the proposed method BDS; a1–a6 original low contrast MRI images and b1–b6 enhanced images by BDS with yellow and blue arrows representing MS lesions

Fig. 9

Original and enhanced images with the proposed method MBDS; a1–a6 original low contrast MRI images and b1–b6 enhanced images by MBDS with green and red arrows representing MS lesions. c1–c6 Original very low contrast MRI images and d1–d6 enhanced images by MBDS with red and green arrows representing MS lesions

Fig. 10

Original image and its corresponding enhanced image using the proposed method MBDS and CSA of the spinal cord; a T2-w MRI of the spinal cord (sagittal section); b equalized image with the proposed method and c T2-w axial view confirming the existence of multiple sclerosis lesion

Fig. 11

Average values of EME, H, and AMBE evaluation metrics for HB dataset

Fig. 12

Average values of PSNR, MSE, SSIM, and FSIM evaluation metrics for HB dataset

Fig. 13

Average values of EME, H, and AMBE evaluation metrics for MSDB dataset

Fig. 14

Average values of PSNR, MSE, SSIM, and FSIM evaluation metrics for MSDB dataset