mini project ppt on brain tumor detection in human brain using mri images

It's about mini project of brain tumor detection

1. BRAIN TUMOR DETECTION USING IMAGE PROCESSING TECHNIQUES SUBMITTED BY: guided by: G. SUKESH-511320104082 mrs. A. manju M.SYED ABDUL RAHMAN-511320104088 S.V. SURJITH KUMAR-511320 104085

2. ABSTRACT: The detection, and extraction of infected tumour from the collected data are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only In this project we aim to use basic image processing steps to detect the brain tumor with the help of dataset in the form of GUI(GRAPHICAL USER INTERFACE). If the detected image is not affected by brain tumor the output will display as status message- “NO TUMOR”. If Detected image is affected by tumor the image processing step stakes place .

3. INTRODUCTION: Brain Tumor is a very serious type of malignancy that occurs when there is an uncontrolled growth of cancer cells in the brain. Brain tumor is caused by a malignant brain tumor. There are two main types of brain tumor. The Primary brain cancer is the rarest type of brain tumor. It can spread and invade healthy tissues on the brain and spinal cord but rarely spreads to other parts of the body. Secondary brain tumor is more common and is causedby a tumor that has begun in another part of the body, such as lung tumor that spreads to the brain .

4. DATA SET: We have collected data set from internet. From this data set we are going to take random images for detection of brain tumor. We use only MRI images of this dataset for the Precise output .

5. EXISTING MODELS This system utilizes image processing techniques, such as segmentation and feature extraction, to automatically detect brain tumors in MRI or CT scans.

6. LITERATURE SURVEY: Md Khairul Islam , Md Shahin Ali, Md Sipon Miah, issued on 15 September,2021. D. Rammurthy , P.K. Mahesh, issued on 10 August, 2020.This paper proposes an optimization-driven technique, namely Whale Harris Hawks optimization (WHHO ) for brain tumor detection using MR images T. Kalaiselvia , P. Kumarashankara , P. Sriramakris hnan , issued on 2019. In this study, we develop a unique technique for combining the colour channels made from multimodal pictures to build an RGB image, highlight the tumour in MRI brain imaging, and find and extract the tumour location from MRI images of people's heads.

7. PROPOSED METHOD: The proposed method combines basic image processing steps within a GUI to facilitate the detection and analysis of brain tumors . 1.Browsing Image. 2.Filtering(Anisotropic). 3.Tumor Alone. 4.Bounding Box. 5.Outline. 6.Location

8. DATA FLOW CHART:

9. OBJECTIVES: The objectives of Process of detection of Brain Tumor using MR image analysis are broadly divided. Study of preprocessing of MR images. Image acquisition Adaptive filtering Study of Image Analysis of MR images Segmentation Feature Extraction Enhancement Development of neural network algorithm for Classification and Detection of Brain Tumor

10. DATA ACQUISITION: The data collected are grouped into two kinds—healthy brain images and unhealthy brain images. Among the 66 patients, 22 patients have normal MRI brain images and the rest 44 collect in the abnormal MRI brain image category from the Harvard Medical School website ( http://med.harvard.edu/AANLIB/ ) The MRI brain image obtained from the database were in the form of an axial plane, T2-weighted, and 256 × 256 pixels. These images are scrutinized, and preprocessing is done before the processing of algorithms .

11. ARCHITECTURE DIAGRAMS Use case diagram Activity diagram Block diagram Overview

12. USE CASE DIAGRAM

13. ACTIVITY DIAGRAM:

14. BLOCK DIAGRAM:

15. OVERVIEW:

16. PREPROCESSING: The preprocessing step focuses on specifically removing the redundancy present in the captured image without affecting the subtleties that play a key role in the general procedure. It is done to improve the visual look and characteristics of an image. In the conventional model, MRI images are often affected by impulse noise, such as salt and pepper, which degrades the performance of the tumor segmentation system to avoid the proposed skull stripping and morphological operations.

17. RESULTS AND DISCUSSION: The suggested strategy was created using MATLAB software that has a Core 2 Duo code configuration. Initially, the preprocessing technique is applied to enhance the image. Next, the segmentation is applied to extract the boundary region of the tumor.

18. CONCLUSION: Medical image segmentation is a challenging issue due to the complexity of the images, as well as the lack of anatomical models that fully capture the potential deformations in each structure. This proposed method works very effectively to the initial cluster size and cluster centers. The segmentation is done by using BWT techniques whose accuracy and computation speed are less. This work recommends a system that requires negligible human intrusion to partition the brain tissue. The main aim of this recommended system is to aid the human experts or neurosurgeons in identifying the patients with minimal time. The experimental results show 98.5% accuracy compared to the state-of-the-art technologies.