Enchodroma Tumor Detection From MRI Images Using SVM Classifier

Introduction

Medical image processing is an important field of research as its outcomes are used for the betterment of health issues. A tumor is an abnormal growth of tissues in any part of the body. As the tumor grows, the abnormal tissue displaces healthy tissue. There is a large class of bone tumor types which have different characteristics. There are two types of bone tumors, Noncancerous (Benign) and Cancerous (Malignant). The benign tumor grows very large and press on nearby tissues, once removed by surgery, they don’t usually reoccur. Malignant tumor has a larger nucleus that looks different from a normal cell’s nucleus and can also reoccur after they are removed. Hence care as to be taken in order to completely avoid tumors. There are different image modalities like X-ray, MRI, CT, PET SCANS has shown in figure 1.1. The MRI imaging technique is the best because it has a higher resolution. Magnetic resonance imaging (MRI) is a non-invasive medical system used to show 2D images of the body. This technique is based on a process that uses highly charged magnetic fields and radio waves to make images of the inside the body. It is an unharmed method of obtaining images of the human body. Its data are most relevant and it helps in early detection of tumors and precise estimation of tumor boundaries. Magnetic resonance (MR) sequences such as T1-weighted, T2-weighted, contrast-enhanced T1W and T2W, STIR (Short T1 inversion recovery), PD-Weighted series provide different information. Thus MRI scans have a best non-invasive medical systems used to show 2D images of the body. This technique is based on a process that used highly charged magnetic fields to make images of the body. Hence MRI has more than one methodology to classify images. These are atlas methods, shape methods, fuzzy methods, and variations methods. New technology MRI are T1 weighted, T2 weighted and proton density weighted images.

Figure 1.

MRI SCAN

The rest of the paper includes section 2 gives the brief glimpse of the relevant work that was carried out all in the various fields of research. Section 3explains segmentation process -thresholding and morphological operations. Section 4 includes the proposed method with results and experimental results. Section 5 includes the conclusions followed by future enhancements.

Review Of Literature

Sinan Onal et al. (Onal et al., 2014) proposed a method of automatic localization of multiple pelvic bone structure on MRI, and they have used an SVM classification and nonlinear regression model with global and local information, and they are presented to automatically localize multiple pelvic bone Durgadevi et al. (Durgadevi & Shekhar, 2015) proposed a method of Identification of tumour using k-means algorithm. The identification of breast cancer from the MRI images is made automatic using K-means clustering and wavelet transform. The human perception at many times may lead to erroneous diagnosis. Variation in diagnosis may produce adverse effect on the patients. Hence to improve the accuracy this system is made automatic using machine vision algorithms. Alan Jose et al. (Emran et al., 2015) described Brain Tumour Segmentation Using K-Means Clustering and Fuzzy C-Means Algorithms and Its Area Calculation; they have given Simple Algorithm for detection of range and shape of tumour in brain MR Images. Normally the anatomy of the Brain can be viewed by the MRI. MRI scanned image is used for the entire process. The MRI scan is more comfortable than any other scans for diagnosis Deepak et al. (Jose et al., 2014) discussed Comparative Study of Tumour Detection Techniques with their Suitability for Brain MRI Images The canny edge detection technique defines edges of the MRI image by using many parameter like thresholding, thinning etc. canny with morphological operation like dilation, erosion etc., where simply applied on it for getting better results, and fuzzy c-means method gives best results for segmentation of Brain tumour in MRI images. M. Koch, et al,(Bagahel & Kiran, 2015) described automatically segmenting the wrist bones in the arthritis patients using the k-means clustering process.