Brain Tumour Diagnosis Using Matlab with Edge Detection

Abstract

The segmenting brain tumours in magnetic resonance images (MRI) is a difficult task due to the variety of possible curves, spots, and image concentrations. Brain tumour segmentation is one of the most critical and challenging projects in the field of medical image processing because human-assisted manual characterization can result in inaccurate prediction and diagnosis. [1] A brain tumour is an unusual mass of tissue in which some cells multiply and grow uncontrollably. Furthermore, it is a difficult task when there is an enormous amount of information to be processed. Because brain tumours have a wide range of manifestations and coexist with normal tissues, extracting tumour regions from images becomes complicated. [2] Medical image processing provides basic information of abnormality of brain and it helps the doctor for best treatment planning. This paper specifically aims to detect and localisation tumour regions in the brain using the proposed methodology and patient MRI images.[3] We can derive detailed anatomical information from these high-resolution images in order to examine human brain development and detect abnormalities. Pre-processing, edge detection, and segmentation are the three stages of the proposed methodology. [4] Several tests are performed on the patient to detect cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the most commonly used tests for locating brain tumours. The pre-processing stage involves the conversion of the original image to grayscale and removing any noise that has crept in. [5]The primary step in removing noise and smoothing an MRI image is pre-processing. Following that, segmentation is used to actually indicate the tumor-affected region in the MRI images. Finally, the watershed algorithm is being used to cluster the image. For the implementation of this system, we used MATLAB. Magnetic Resonance Imaging (MRI) has increased in popularity as a high-quality medical imaging technique. [6] The experimental results demonstrated that the proposed approach outperformed existing available approaches in terms of accuracy while maintaining the pathology experts\' acceptable accuracy rate. Magnetic resonance imaging (MRI) is a specialized diagnostic imaging technique that provides comprehensive information about human soft tissue anatomy. This methodology allows for extensive clinical practice in the detection of brain tumours, making it simple to identify patients predicated on MR image data. In this paper, we propose a MATLAB programming technique for separating tumour images from brain magnetic resonance (MR) data.[7] The goal of segmentation is to simplify and/or change an image\'s representation into something more meaningful and easier to analyse. The accuracy of tumour detection is highly noticeable in the MRI image data, and the tumour is clearly highlighted using the proposed MATLAB Coding. These codes are used to enhance the MR image quality by trying to adjust the grey level and applying additional special filters. The MRI dataset confirms that the algorithm\'s outcomes are more applicable to ordinary output images to identify brain tumours.

Introduction

I. INTRODUCTION

Digital image processing is a developing field in which doctors and surgeons are discovering new and simple ways to analyze complex diseases such as cancer, brain tumours, breast cancer, kidney stones, and so on. Because the human body has anatomical structure by nature, identifying and segmenting brain tumours is a tedious and time-consuming task. The detection of brain disease is a difficult task that requires special attention to image segmentation.[8] A proposed method for estimating tumour affected area using Watershed Segmentation. The location of the tumour was same in their approach, but there was no information on the collection of images tested or the accuracy rate. In the discussed applications of image analysis and techniques such as MRI, CT scan, and X-rays, a specific part of the body is scanned. Physicians or surgeons evaluate the images in order to solve the problems. Brain tumours are a major cause of mortality and morbidity worldwide, and the affiliated abnormalities cause significant life changes.[9] propose an automated and efficient brain tumour detection technique based on Magnetic Resonance Imaging (MRI) images that integrates two image segmentation methods: modified extracting features region growing and cellular automata edge detection.

A brain tumour is an abnormal growth of tissues that develops uncontrollably and unchecked by the checkpoints that help regulate cell growth. A primary or metastatic brain tumour can reside. Metastatic tumours are those that have spread from other parts of the body to the brain. A brain tumour is one of the most common causes of death in adults and children.

[10] Tumor segmentation is the most important task in classification because all decisions are based on the segmented tumour. Brain tumours account for one-quarter of all cancer deaths in children. Every year, approximately 200 different kinds of tumours are diagnosed in the United Kingdom. Medical resonance imaging is a cutting-edge imaging technique that produces high-quality images of human anatomy. Anatomical analysis of brain development and abnormalities is executed using MRI. MRI is a vital type of technology that uses magnetic waves to create a picture of the body. The harmful cells to a damaged healthy cell and continual growth level to the last stage encased to full brain cell that is also well known as the tumour is the most common brain disease. A brain tumour is an uncontrolled growth of tissue in a variety of body parts. [11] Primary brain tumours are divided into two types: benign and malignant. Benign tumours can be removed and rarely recur. Benign brain tumours typically have a distinct border or edge. They are not contagious and do not spread to other parts of the body. The analysis of the anatomical structure of the brain MRI image data to the estimation of tumour image, such as MATLAB algorithms, shape, size detection of tumour the analysed different types of tumours characteristics and full right way treatment. The tumour is a group of abnormal cells in the brain that are growing in number, particularly in the frontal lobe. These MRI images are pre-processed so that additional morphological operations can be performed on them to detect tumour size, shape, and location. MRI images are pre-processed for noise removal and image enhancement, and morphological process is carried out using MATLAB algorithms to detach and detect tumours in the brain. Segmentation's ultimate goal is to extract important features from image data. The segmentation of tumours from brain MRI images is a time-consuming process   MATLAB is a fast algorithm which can detect tumours from MRI images in a very short period of time. Finally, the tumour is mapped with 255 intensity onto the original greyscale image to make it visible in the image. It is highly unpredictable the size and shape of tumour wounds in the brain which can be seen in the MRI images Normal cells in the brain are quickly affected by brain injuries.

We adopt a multi-spectral structural approach based on its similarity. It is necessary to invest time and effort over a period of time in order to collect and develop inter structural and functional data. Other challenges In which the MRI images of the tumour can be detected using a structural single spectrum. This technique has been successful in detecting slices and reducing tumours. The estimation of images is determined by the organization and clear classification of Brain MRI images, and various techniques have also been introduced. To remove distortion identified with the physical structure and potential very low abnormal tissues given by the mention of a brain tumour on MRI scan data, the initiating system utilizes an adaptive pillar, which surpasses traditionally classified methods. To support the real sources, the operating systems have been accepted and completed. This proves experimental results of improved performance measured by brain MRI. It has succeeded in the techniques of brain tumours to segment him with computations and complexity A study of effectiveness is one of the features of valet statistics.

II. LITERATURE SURVEY

The brain tumour tissues are deformed to know that tumour cell growth levels to estimated anatomical structures of the brain take place more information to known brain diseases with software-based information sharing and simulation analysis.

The following papers were reviewed and momentarily discussed in relation to our research work.

The MRI image dataset was collected from a publicly available source

The sources contain 150 brain MRI images, 75 of which have tumours and the other 75 of which do not.

The research looked into the potential use of MRI data for improving brain tumour shape approximation and 2D & 3D visualisation for surgical planning and tumour assessment. The simulation software implements biochemical and morphological knowledge to solve problems in this comparison result for Images of brain tumours. This is the most important part of the body and the most complicated organ of the human brain nervous system, so consider using MRI as a very soft process of talking information source.

III. METHODOLOGY

With the help of existing algorithms, a brain tumour and system software will be written and modelled in the Matlab image processing tool. Organizing this system tends to take MRI image data as an input source and goes to math works in MATLAB processing such as a change to RGB to Gray and increase decrease of grey levels (0 to 255) (255 or > 255) grey is transformed to black and white images that show needed information in the images. The diagrams show a block diagram of detecting systems in action. The illuminated result is a black and white data set with properly segmented tumours.

A. Tumor Image Database

The 150 tumour images of both normal and abnormal brain are collected from various hospitals such as Fortis, Mulund, and many more via research sources such as websites, and are stored in a database. One of the images is taken as from database and used for tumour detection.

B. Grayscale Image

Grayscale images are the favored method for image processing because they are much less complex and allow for a detailed discussion of contrast, brightness, and edges without giving due consideration colors. Colors are much more complex to their three channels, which is why images are converted to grayscale before further processing in image segmentation.

The image contains speckle noise and has a low contrast. As a result, the image quality may be inadequate for analysis.

It is critical to identify the location of the tumour before undergoing surgical procedures. Image restoration pre-processing is required to overcome speckle noise and low contrast.

C. Morphological Operations

Top hat filter is used to highlight the sharp peaks and gradients in image. Apply morphological operations in which ‘strel’ i.e. structuring element of disk shaped is taken and applied on the grayscale image for extracting the tumor from MR image of patients(Deng, Xiao et al. 2009). The tumor obtained in the above step has no clear edges or boundaries but it has somehow sharpened the tumor in the image.

In this paper, we will investigate image estimation of brain anatomical Magnetic resonance imaging to the most essential parameter executes seeing so that the designated need-able detected dataset segment can be completed. The tumour ages, and growth cells automatically underline the tumor's brain disease. 

This actual MR Image introducing abnormal cells shown visualized data has highlighted such zooming levels fully sharpness to using filters in MATLAB works.

D. Watershed Segmentation

Previously, segmentation was done manually, but it is a time-consuming and labor-intensive process. As a result, this same process was automated, but the results were still unsatisfactory.

Some of the brain tumour MR images were used to test the proposed methodology, which employs a hybrid approach for image segmentation that combines the top hat filter and the watershed algorithm. One‘s example images, however, are simple MRI scans. However, on more complex images, the proposed algorithm produced not so good results. Our proposed method, on the other hand, yields promising results in more complicated cases. 

After increasing the applied pass filter, the watershed image is overlaid over the image. Watershed transformation is being used to segment the cells in an image. It is applied to a grayscale image using MATLAB watershed algorithms.

For this project, real-world patient data from the 'brain web' is analyzed. Because the tumour in MR images has a higher intensity than the background, it is simple to locate and extract the tumour from the image.

IV. SIMULATED RESULT

Magnetic resonance imaging is used to scan the human brain for known brain diseases including such metastatic brain tumours, also known as secondary brain tumours, which are caused by cancer cells spreading to the brain from some other part of the body.

Because that tumour had an effect on normal cells, conduct the analysis to find the tumours. Cancer cells break away from the primary tumour and travel to the brain, usually via the bloodstream, in which they are most commonly found in the cerebral hemispheres or the cerebellum. Cancer can spread to metastatic spine tumours as well.

Metastatic brain tumours are five times more common than primary brain tumours, which occur in the brain. Taken to software works distinguishing the images data result that search disease of the brain to best performance accuracy seeing the tumour to taken MRI data.

V. FUTURE

As stated in the methodology section, the above findings were achieved using various algorithms in MATLAB. These results were obtained sole intention of detecting the precise location of tumours in MRI images from a real data set of patients. There are a variety of other techniques that can be used for segmentation. For tumour detection, each of these methods utilises a different algorithm in MATLAB. This field has a lot of immense potential. If further developed, this work could be used to detect tumour size, stage, and type using image processing techniques.

With the help of 3-D modeling, this prototype can be further implemented. In this field, the use of various rapid algorithms would be appreciated. Having the ability to get and detect multiple MR images at the same time would save time.

VI. RESULT

1. Load Image: A brain MRI image is taken as an input with the help of system software.

2. Pre-Processing

The image contains speckle noise and low contrast. Thus image may be inadequate for analysis thus, it is processed via MATLAB image processing tool.

3. Watershed Segmentation

As segmentation is time consuming and labor-intensive watershed algo. Is preferred as it yields the image It further segments the cell in an image.

4. Tumor Alone

Tumor is outlined ad required data has been plotted on the screen.

5. Tumor Area Location

Same tumor image is shown along with entire brain scan image further the tumor image’s location is now highlighted.

6. Tumor Outline

An outline of tumor detected is presented on screen defining the specific area.  

This technique is used to produce the desired result, such as brain tumour detection through software-based estimation of feature representation, enhancement, sharpness, and filtering is well known brain tumours and brain diseases. To assess the efficacy of the proposed scheme, we applied the density-based morphological brain MR Image segmentation method to a number of alternative MR images.

The proposed algorithm is applied to these images (grayscale and colour), aerial images, and high-resolution images. If the original image is colourful, the image can be obtained after the RGB to grey conversion. After that, the image is sharpened and enhanced. After segmentation algorithm, the resulting image is displayed.

Edge detection yields fine gradient magnitude edges, which are then used in the morphological operation, and the result is displayed. The MATLAB coding achieving grey level-based visualisation in the MR Image lowering and rising grey levels to sharpness and brightness in the images shows tumour ages growth inside the brain anatomical structure MR images. Best performance and accuracy based on an image datasets.

VII. ACKNOWLEDGEMENT

I would like to thank Prof. Vijay Bhosale , Professor Department of Computer, M.G.M College of Engineering, University of Mumbai, India, for consistently inspiring us to do research and assisting us in accomplishing the desired goals.

Conclusion

A brain tumour is a collection of abnormal cells that grow inside and around the brain. The accuracy of tumour detection is clearly visible in the MRI image data, and the tumour is clearly highlighted using the proposed MATLAB Coding. MR image segmentation is a critical but difficult problem in medical image processing. In general, it cannot be solved using simple, traditional image processing techniques. The development of automated algorithms is difficult due to the characteristics of MR images. The MATLAB simulation is performed on various brain images, and tumour detection is performed for image segmentation and optimal global thresholding. The detection of brain tumours is a great help to physicians and a boon to medical imaging and industries involved in the production of CT Scan and MRI imaging. The MRI dataset illustrates that our Algorithm results are more relevant in detecting brain tumours on average output images. This type of approach is available for clinical ground truth estimation. Results are practically procured in the ideal aspect.

References

[1] Hazra, A. Dey, S. K. Gupta and M. A. Ansari, \"Brain tumor detection based on segmentation using MATLAB,\" 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017, pp. 425-430, doi: 10.1109/ICECDS.2017.8390202. [2] A. R. Matthew, A. Prasad and P. B. Anto, \"A review on feature extraction techniques for tumor detection and classification from brain MRI,\" 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2017, pp. 1766-1771, doi: 10.1109/ICICICT1.2017.8342838. [3] Patil RC, Bhalchandra AS. Brain tumour extraction from MRI images using MATLAB. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE). 2012 Apr 1;2(1):1. [4] Saini, Pankaj Kr, and Mohinder Singh. \"Brain tumor detection in medical imaging using MATLAB.\" International Research Journal of Engineering and Technology 2.02 (2015): 191-196. [5] Oo, Swe Zin, and Aung Soe Khaing. \"Brain tumor detection and segmentation using watershed segmentation and morphological operation.\" International Journal of Research in Engineering and Technology 3.03 (2014): 367-374. [6] Rajan, P. G., and C. Sundar. \"Brain tumor detection and segmentation by intensity adjustment.\" Journal of medical systems 43.8 (2019): 1-13. [7] Karuna, M., and Ankita Joshi. \"Automatic detection and severity analysis of brain tumors using gui in matlab.\" International Journal of Research in Engineering and Technology 2.10 (2013): 586-594. [8] Sujan, Md, et al. \"A segmentation based automated system for brain tumor detection.\" International Journal of Computer Applications 153.10 (2016): 41-49. [9] S. Charutha and M. J. Jayashree, \"An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection,\" 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014, pp. 1193-1199, doi: 10.1109/ICCICCT.2014.6993142. [10] Baraiya, Neha, and Hardik Modi. \"Comparative study of different methods for brain tumor extraction from MRI images using image processing.\" Indian Journal of Science and Technology 9.4 (2016): 1-5. [11] Sharma, Pratibha, Manoj Diwakar, and Sangam Choudhary. \"Application of edge detection for brain tumor detection.\" International Journal of Computer Applications 58.16 (2012).

Copyright

Copyright © 2022 Omkar Maruti Gaonkar, Nitesh Sandip Jadhav, Dishant Krishna Koli, Prof. Vijay Bhosale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.