A Hybrid Segmentation Model based on Watershed and Gradient Vector Flow for the Detection of Brain Tumor
نویسندگان
چکیده
Medical Image segmentation deals with segmentation of tumor in CT and MR images for improved quality in medical diagnosis. Geometric Vector Flow (GVF) enhances the concave object extraction capability. However, it suffers from high computational requirement and sensitiveness to noise. This paper intends to combine watershed algorithm with GVF snake model to reduce the computational complexity, to improve the insensitiveness to noise, and capture range. Specifically, the image will be segmented firstly through watershed algorithm and then the edges produced will be the initial contour of GVF model. This enhances the tumor boundaries and tuning the regulating parameters of the GVF snake mode by coupling the smoothness of the edge map obtained due to watershed algorithm. The proposed method is compared with recent hybrid segmentation algorithm based on watershed and balloon snake. Superiority of the proposed work is observed in terms of capture range, concave object extraction capability, sensitivity to noise, computational complexity, and segmentation accuracy.
منابع مشابه
MULTI CLASS BRAIN TUMOR CLASSIFICATION OF MRI IMAGES USING HYBRID STRUCTURE DESCRIPTOR AND FUZZY LOGIC BASED RBF KERNEL SVM
Medical Image segmentation is to partition the image into a set of regions that are visually obvious and consistent with respect to some properties such as gray level, texture or color. Brain tumor classification is an imperative and difficult task in cancer radiotherapy. The objective of this research is to examine the use of pattern classification methods for distinguishing different types of...
متن کاملA Hybrid 3D Colon Segmentation Method Using Modified Geometric Deformable Models
Introduction: Nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. One of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis. Materials and Methods: In this work, a hybrid method based on Geometric Deformable Models...
متن کاملDiagnosis of brain tumor using PNN neural networks
Cells grow and then need a very neat method to create new cells that work properly to maintain the health of the body. When the ability to control the growth of the cells is lost, they are unconsidered and often divided without order. Exemplified cells form a tissue mass called the tumor. In fact, brain tumors are abnormal and uncontrolled cell proliferations. Segmentation methods are used in b...
متن کاملDetection of Glioblastoma Multiforme Tumor in Magnetic Resonance Spectroscopy Based on Support Vector Machine
Introduction: The brain tumor is an abnormal growth of tissue in the brain, which is one of the most important challenges in neurology. Brain tumors have different types. Some brain tumors are benign and some brain tumors are cancerous and malignant. Glioblastoma Multiforme (GBM) is the most common and deadliest malignant brain tumor in adults. The average survival rate for peo...
متن کاملA Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis
Accurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In this research, a novel fuzzy approach has been proposed to classify a given MRI brain image as normal or cancer label and the i...
متن کامل