Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area

Abstract

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting

“…Each patch has an initial size of 200 × 200 pixels, which was scaled up to three times larger for processing. Each enlarged patch was rst smoothed using an averaging lter followed by morphological operations which include; low intensity elimination; conversion to multiple binary images using threshold values of 0.1, 0.3, 0.5, 0.7, 0.9, and Otsu's threshold value [22]; elimination of small objects; lling holes; object counting; continuous erosion using an incremental disk until the number of binary objects was equal to one object; and region growing using the centre of the detected binary object as the seed point [23]. ese operations were repeated with an additional initial step of intensity adjustment between 30% and 70% in order to increase the contrast of all possible tumors and eliminate false dark regions.…”

“…A brain tumor segmentation technique using ant colony optimization (ACO) was proposed by Kullayamma et al [21]. A new approach to improve the segmentation accuracy of brain tumors based on temperature pro les changes in the tumorous region was proposed Bousselham et al [22]. In this approach, Pennes bioheat equation and Canny edge detection method were used to estimate tumor contours based on the change of temperature.…”

“…On the other hand, if the image has no object, then there are no more tumors in the original image. Once a possible tumor is detected, it is eliminated from the original MRI image and the object counting process is repeated in order to detect and localize all possible tumors in the MRI image [22].…”

“…If it is found to be less than a prede ned threshold, that possible tumor is considered as a false tumor. On the other hand, if the Euclidean distance is greater than the prede ned threshold value, the possible tumor is considered as a true tumor in the MRI image [22].…”

Performance Analysis of Glioma Brain Tumor Segmentation using Ridgelet Transform and CANFES Methodology

“…The sparse directionality function can be achieved by Finite Ridgelet Transform (FRIT). The functional steps in this Ridgelet transform are given as, (1). Computing the discrete radon function;…”