Automated Localization of Brain Tumors in MRI Using Potential-K-Means Clustering Algorithm

Abstract

The manual localization and precise segmentation of brain tumors from magnetic resonance images (MRI) is time-consuming and error-prone. In T2 and FLAIR MRI, tumors appear as bright areas of higher signal intensity than their surroundings. In this paper we view the intensity of a pixel as equal to its "workload" and employ an unsupervised learning algorithm called potential-K-means that generates a balanced distribution of the pixels into clusters of approximately equal total intensity. The algorithm is based on an analogy with the gravitational force exerted by masses. This balancing requirement introduces a search bias that tends to generate either small clusters of higher intensity pixels, which overlap with the tumor area, or large clusters of lower intensity pixels. We evaluate the proposed algorithm on the publicly available brain tumor image segmentation (BRATS) MRI benchmark by comparing the center of the cluster that overlaps with the tumor, with the center of the tumor in the corresponding ground truth segmentation. We compare the proposed algorithm with the well-known K-means and with the Force clustering algorithm by Kalantari et al. (2009), which follows a different Physics analogy, but it is also based on a balancing criteria. Experimental results show that K-means is not suitable for tumor localization and that potential-Kmeans and Kalantari's approach are comparable. However, the performance of Kalantari's approach is highly dependent on a parameter whose value needs to be set a priori, but without an informed way of doing so, which makes the present proposed method more practical.

MRI images Enhancement and Brain Tumor Segmentation

“…The preliminary results also demonstrate the effectiveness and efficiency of our 5-step approach to brain tumor detection and detection and extend this framework to detect other types of tumors in other types of medical images and motivate them to be localized [11]. Cabria et al (2015) proposed "Automated Localization of Brain Tumors in MRI Using Potential-K-means Clustering Algorithm". In this paper, they viewed the intensity of a pixel as equal to its "workload" and employed an unsupervised learning algorithm called potential-K-means that generates a balanced distribution of the pixels into clusters of approximately equal total intensity.…”

“…In this paper, they viewed the intensity of a pixel as equal to its "workload" and employed an unsupervised learning algorithm called potential-K-means that generates a balanced distribution of the pixels into clusters of approximately equal total intensity. A set of 22 images of the FLAIR MRI (axial plane) modality from the BRATS dataset was used [12]. S. Priyanka, Dr. AS Naven kumar proposed the name of the noise elimination document "Noise Removal in Remote Sensing Image Using Kalman Filter Algorithm " in 2016.…”

Brain Tumor Segmentation Based on Deep Learning’s Feature Representation

“…The methods [18,27,29] of tumor segmentation use the ground truth in their algorithms to obtain a high-performance result and during evaluation. On the other hand, the methods [30][31][32] are shallow unsupervised methods for tumor segmentation without using the ground truth; our method belongs to these unsupervised ones based on the features extracted from the CNN and the post-processing to reach our objective of tumor segmentation. From Table 6, we can say that our method is better than the unsupervised methods that exist and is of the same performance as supervised methods based on deep learning.…”

Automated Brain Tumor Segmentation in MR Images Using a Hidden Markov Classifier Framework Trained by SVD-Derived Features

“…In brain tumor segmentation, two clusters are isolated in the image by labeling the pixels as either healthy or cancerous by calculating some features [7]. The conventional features for classification are the pixel intensity, depth, color and texture [8].…”