Digital image processing is vast fields which can be using various applications. Which include Detection of criminal face, fingerprint authentication system, in medical field, object recognition etc. Brain tumor detection plays an important role in medical field. Brain tumor detection is detection of tumor affected part in the brain along with its shape size and boundary, so it useful in medical field. Segmentation and the subsequent quantitative assessment of lesions in medical images provide valuable information for the analysis of neuropathologist and are important for planning of treatment strategies, monitoring of disease progression and prediction of patient outcome. For a better understanding of the pathophysiology of diseases, quantitative imaging can reveal clues about the disease characteristics and effects on particular anatomical structures

1. A Deep Learning Approach for Semantic Segmentation in Brain
Tumor Images
Under the esteemed guidance of
Mr.A.Hari Prasad Reddy

2. A Deep Learning Approach for Semantic Segmentation in
Brain Tumor Images
• P. Nanda Sai (18R11D5803)

3. ABSTRACT
• In medical technology, Magnetic Resonance Imaging (MRI) has been used widely for
• detection of tumors and diagnosing various abnormalities in tissues.
• In scientific research, a major role has been played by the active development in the
computerized segmentation of medical image.
• Based on fast decision making, the doctors can take required treatment easily.
• In the information technology, the segmentation of brain tumor is a key point.
• By analyzing the radiation therapy treatment, tumor growth, computer-based surgery,
developing the growth models of tumor, and treatment responses, the segmentation of brain
tumor is motivated.
• For segmentation of brain tumor, a deep learning-based framework is presented. To achieve
the robust performance through a majority rule, deep learning based semantic
• segmentation architecture is used for segmentation of tumor that can improve the
performance effectively.

4. PROPOSED SYSTEM
• Pre-processing
In the experiment, the basic pre-processing techniques are used. The cropping and resizing of
images are done with the use of suitable schemes of cropping (NDVI image) in order to fulfil
the needs. To provide uniform intensity, the image is normalized later. The filtration of an
image is also accomplished based on low pass filter.

5. • Colour feature extraction
Color is an important and the most straight-forward feature that humans
perceive when viewing an image. Human vision system is more sensitive to
color information than gray levels, so color is the first candidate used for
feature extraction.
The following four models are used in a color image retrieval system.
The RGB color model
The HSV Color Model
The HMMD color space
The CIE lab color space

6. • Semantic segmentation
Pixel-wise image segmentation is a well-studied problem in computer vision. The
task of semantic image segmentation is to classify each pixel in the image.
Semantic image segmentation is the task of classifying each pixel in an image
from a predefined set of classes.

7. SYSTEM REQUIREMENTS

8. HARDWARE REQUIREMENTS
• System : Intel i3 2.5 GHz.
• Hard Disk : 250 GB
• Monitor : 15 VGA Colour.
• Mouse : Optical Mouse
• Ram : 4-8 GB

9. SOFTWARE REQUIREMENTS
• Operating system : Windows 7/8/8.1/10.
• The technology selected for implementing this project is machine learning.
• The development was done in a ‘windows operating system’ using MATLAB

10. SCOPE OF THE PROJECT
• Image processing commonly refers to virtual photo processing, however optical and analog
photograph processing are also possible.
• This article is set standard strategies that apply to they all. The acquisition of pictures
(producing the input image within the first place) is referred to as imaging.

11. MODULES
In particular, digital photo processing is the simplest sensible technology for:
• Classification
• Feature Extraction
• Pattern Recognition
• Projection
• Multi-scale signal evaluation

12. LITERATURE OVERVIEW
MATLAB:MATLAB is an interactive, matrix-oriented program package for calculation,
visualization and Programming scientific-technical questions.
MATLAB, an acronym for Matrix Laboratory, was created by Cleve Moler and
Jack Little in the 70s Years developed and distributed by their company "The
MathWorks, Inc" (www.mathworks.de). It is one of the world's best-known tools
for calculation, visualization and programming complex mathematical and
technical problems. It was originally used as an interface LINPACK and EISPACK
developed.

13. The functionality of MATLAB can be used in two ways: on the one hand as
interactive Calculation and simulation environment (MATLAB) is a very powerful calculator) and for
others about calling MATLAB programs. The MATLAB own programming language is an interpreter
language and largely uses the well-known mathematical notation. Although MATLAB normally calculates
numerically, there are also interfaces to computer algebra Systems like Maple, a toolbox for algebraic
computing, as well as the possibility of code in Embed Fortran, Java, C or Pearl.MATLAB has a modular
structure. In addition to the basic module (MATLAB itself), there are many on toolboxes which other
functionalities for the most different areas of application Make available.

14. SYSTEM ARCHITECTURE

15. Use case Diagram

16. SAMPLE CODE
• Test.m
clc;
clear;
close all;
file = uigetfile ('.jpg','Select Brain tumor image');
im = imread(file);
[H, W, m] = size(im);
a = im;
b = im;
im (:2) = a;
I’m (:3) = b;
figure, show (I’m), title ('Input image');

17. op = zeros (H, W);
k=1;
load net;
for i = 1:H
for j = 1: W
px = im (i, j, :);
var (: k) = px (:);
k = k+1;
end
End
predicted Labels = classify(net,var);
n=1;
for i = 1:H
for j = 1:W
op (i, j) = predictedLabels(n);
n=n+1;
end
End
op_ref = ones(size(op));
op_final =(op-op_ref);
bw = bwareaopen(op_final,600);
figure,
imshow(bw); title ('Binarized output');
mask = bw;
figure,
imshow(labeloverlay(im,mask)); title('Semantic segmentation output');

18. TESTING
• Testing is the process of evaluating the software to check whether all the
requirements from the users or customers have been gathered and executed. The
process of testing consists of software validation and software verification.
• Software validation
Software validation is the process of checking whether the software satisfies the
user requirements. The validation process will be a success if all the requirements
of the user meet correctly.
• Software verification
Verification is carried out to confirm whether the software meets the business
requirements and it is developed keeping in mind the proper specifications and
methodologies.

19. TEST CASES
Test cases are sets of actions that is used to test if a feature of software application
is running correctly. Test cases contain test data, test steps, precondition and post
condition. Test cases also include specific conditions and variables through which
expected results and actual results can be compared to test whether the software is
working as per the requirements of the customer.

20. Test case : Fuzzy clustering (FMC)
Input : Image dataset
Process : The algorithm is trained with dataset to finding the cluster
centers by iteratively adjusting their positions and minimizing an
objective function.
Output : the image clustering is tested manually, and taking extra
region, loss and accuracy and percentage
Test case : Region growing
Input : Test dataset
Process : Region growing, or region merging is a procedure that looks
for groups of pixels with similar intensities.

21. Output : The major drawbacks of these methods are over segmentation,
sensitivity to noise, poor detection of significant areas with low
contrast boundaries, and poor detection of thin structures, etc.
Test case : Semantic segmentation
Input : Image dataset
Process : The algorithm is tested in order to Pixel-wise image
segmentation is a well-studied problem in computer vision. The task
of semantic image segmentation is to classify each pixel in the
image.
Output : In brain tumor detection Semantic segmentation containing
percentage of accuracy of target class and output

22. OUTPUT SCREENS
Main Screen

23. Shows the different brain tumor images.

24. shows the binarized image by mapping labels into original size, then get the
binarized output.

25. shows the semantic segmented images. Semantic segmentation generates
colours automatically for region of interest parts.

26. FURTHER ENHANCEMENTS
• In future, this technique can be developed to classify the
• tumours based on feature extraction.
• This technique can be applied for ovarian, breast, lung, skin
tumours.
• Instead of rectangular boxes, can work with general boundaries:
level set based framework.

27. CONCLUSION
• At the end of the application the client gets easy and fast
response for the problem that has occurred with proper
recommendations. The application is user-friendly. It gives
better performance to the admin so that he can easily add
management and view clients and management. The client can
post any query of his problem at any time and can get the
solution for that. Time is more consumed with the application
rather than searching for the problem in the manual book.

28. BIBLIOGRAPHY
BOOKS REFERENCES:
• Giger, Maryellen L. “Machine learning in medical imaging” Journal of the
American College of Radiology 15, no. 3 (2018): 512-520.
• Mohsen, Heba, El-Sayed A. El-Dahshan, El-Sayed M. El-Horbaty, and
Abdel-Badeeh M. Salem. “Classification using deep learning neural
• networks for braintumors” Future Computing and Informatics Journal 3,
no.1 (2018): 68-71..
• Speight, Paul M., Syed Ali Khurram, and Omar Kujan. “Oral potentially
malignant disorders: risk of progression to malignancy.” Oral surgery, oral
medicine, oral pathology and oral radiology 125, no. 6 (2018): 612-627.

29. THANK YOU
P. Nanda Sai 18R115803)