Abstract

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify in MRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images. This research proposed the new framework of the hybrid k-Nearest Neighbors (k-NN) model that is a combination of hybridization of Graph Cut and Support Vector Machine (GCSVM) and Hidden Markov Model of k-Mean Clustering Algorithm (HMMkC). There are four different methods are used in this research namely (1) SVM, (2) GrabCut segmentation, (3) HMM, and (4) k-mean clustering algorithm. In this framework, on the one hand, phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance. This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scale-invariant features transform. On the other hand, in phase two, segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode (ICMM) with identifying the range of distant. Comparative evaluation is also performing by the comparison of existing techniques in this research. In conclusion, our proposed model gives better results than existing. This proposed model helps to common man and doctor that can identify their condition of brain easily. In future, this will model will use for other brain related diseases.

Highlights

  • The concept of “K-Nearest Neighbor (k-NN)” is one of the simplest, easy-to-understand, and interprets non-parametric machine learning algorithms

  • To evaluate the performance of the proposed hybrid Graph Cut and Support Vector Machine (GCSVM) technique for classification of support vector machine (SVM) and graph cut algorithm technique and image segmentation to diagnosing the tumor or cerebral spinal fluid (CSF) fluid in the image which is given below: Figs. 2a–2c show the features of pixels corresponding to background, white matter, grey matter, tumor, and CSF Fluid in magnetic resonance imaging (MRI) images which is develop by the strategy of SVM and k-mean algorithm

  • The technique was implemented on the MRI images of CSF fluid with low-grade tumor to improve the performance on the trained datasets by the proposed technique

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Summary

Introduction

The concept of “K-Nearest Neighbor (k-NN)” is one of the simplest, easy-to-understand, and interprets non-parametric machine learning algorithms It gives relatively high and competitive results. The nearest neighbor algorithm concept defines the “k” points which are nearest to the unidentified sample in the multidimensional space, sorts them, and assigns a class based on how closely it matches the k points [3]. The Euclidean distance is used to evaluate the nearest neighbor of any instance along with recalculating the new group “k” points [4,5]. This research extends the method by using the HMM so that the tumor classification based on previous and subsequent segmentation results will be concluded This approach uses probabilistic reasoning over time and space for brain tumor segmentation from 4D MRI [8–12]. It is assumed that multi-dimensional (4D) segmentation has been widely used in the image segmentation field and proven to be effective [13–15]

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