Medical Image Processing is a complex and challenging field nowadays. Processing of MRI images is one of the parts of this field. This paper proposes a strategy for efficient detection of a brain tumor in MRI brain images. The methodology consists of the following steps: preprocessing by using sharpening and median filters, enhancement of image is performed by histogram equalization, segmentation of the image is performed by thresholding. This approach is then followed by the further application of morphological operations. Finally the tumor region can be obtained by using the technique of image subtraction.

Natarajan P

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Krishnan V

Centre for Bio-Separation and Technology

Kenkre N.S

Shraiya Nancy

Singh B.P.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Tumor detection using threshold operation in MRI brain images

2012 IEEE International Conference on Computational Intelligence and Computing Research

The quality of health services provided by medical centers varies widely, and there is often a large gap between the optimal standard of services when judged based on the locality of patients (rural or urban environments). This quality gap can have serious health consequences and major implications for patient’s timely and correct treatment. These deficiencies can manifest, for example, as a lack of quality services, misdiagnosis, medication errors, and unavailability of trained professionals. In medical imaging, MRI analysis assists radiologists and surgeons in developing patient treatment plans. Accurate segmentation of anomalous tissues and its correct 3D visualization plays an important role inappropriate treatment. In this context, we aim to develop an intelligent computer-aided diagnostic system focusing on human brain MRI analysis. We present brain tumor detection, segmentation, and its 3D visualization system, providing quality clinical services, regardless of geographical location, and level of expertise of medical specialists. In this research, brain magnetic resonance (MR) images are segmented using a semi-automatic and adaptive threshold selection method. After segmentation, the tumor is classified into malignant and benign based on a bag of words (BoW) driven robust support vector machine (SVM) classification model. The BoW feature extraction method is further amplified via speeded up robust features (SURF) incorporating its procedure of interest point selection. Finally, 3D visualization of the brain and tumor is achieved using volume marching cube algorithm which is used for rendering medical data. The effectiveness of the proposed system is verified over a dataset collected from 30 patients and achieved 99% accuracy. A subjective comparative analysis is also carried out between the proposed method and two state-of-the-art tools ITK-SNAP and 3D-Doctor. Experimental results indicate that the proposed system performed better than existing systems and assists radiologist determining the size, shape, and location of the tumor in the human brain. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Multimedia Tools and Applications

An efficient computerized decision support system for the analysis and 3D visualization of brain tumor

Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than any of the input images to retaining the important features of each image. The resulting image contains more information as compared to individual images. Multiple image fusion is an important technique used in aerial and satellite imaging, medical imaging, robot vision, digital camera application and battle field monitoring. The histogram equalization method is useful in images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to better views of functional and structural parts in MRI and CT images. Based on this, the removal of noise in the image by using adaptive filtering, the system is evaluated and the accurate output will be produced. The fusion algorithm of foreground images and background images is studied. Here, the foreground image is MRI and background image is CT image.

Fusion of MRI and CT brain images using histogram equalization

CVGIP: Image Understanding

Digital image processing (2nd ed.)

Journal	Data powered by Typeset2012 IEEE International Conference on Computational Intelligence and Computing Research
Publisher	Data powered by TypesetIEEE
Open Access	0