Medical Thesis Topics

Medical Thesis Topics enables quantitative analysis and visualization of medical images (modalities) such as CT, PET and MRI. Imaging may become an essential components in many fields of biomedical engineering. Medical image data’s are used to collect information about physiological processes or organs of the body. Medical imaging thesis concepts are developed based on MATLAB simulation tool.

 

Requirements of Medical Imaging:

1.Area calculations of the cells of a biomedical image.

2.Image Enhancements.

3.Manipulating of colors within an image.

4.Construction of 3D images from 2D images.

 

Process of Medical Image Processing:

1.Choose an object.

2.Imaging device used to capture DICOM image.

3.Data conversion (DICOM to other modalities).

4.Data to be progressed by algorithms.

5.Getting reconstructed cross sectional image as result.

AREAS  OF MEDICAL THESIS TOPICS:

N

Segmentation of anatomical structure.

N

Stage analysis of asthma

N

Image Registration

N

Kidney stone detection

N

3D Modeling

N

Brain Tumor detection

N

Retinal Blood Vessel Segmentation

N

Knee-Joint Articular Cartilage Segmentation

SAMPLE CODE FOR MEDICAL THESIS TOPICS ON SKIN LESION SEGMENTATION:

% Read image
im = im2double(imread(‘th3.jpg’));
% Convert RGB to Gray via PCA
lab = rgb2lab(im);
f = 0;
wlab = reshape(bsxfun(@times,cat(3,1-f,f/2,f/2),lab),[],3);[C,S] = pca(wlab);
S = reshape(S,size(lab));
S = S(:,:,1);
gray = (S-min(S(:)))./(max(S(:))-min(S(:)));
% Morphological Closing
se = strel(‘disk’,1);
close = imclose(gray,se);
% Complement Image
K= imcomplement(close)
%% 2-D wavelet Decomposition using B-Spline[cA,cH,cV,cD] = dwt2(K,’bior1.1′);
%% Otsu thresholding on each of the 4 wavelet outputs
thresh1 = multithresh(cA);
thresh2 = multithresh(cH);
thresh3 = multithresh(cV);
thresh4 = multithresh(cD);
%% Calculating new threshold from sum of the 4 otsu thresholds and dividing by 2
level = (thresh1 + thresh2 + thresh3 + thresh4)/2;
% single level inverse discrete 2-D wavelet transform
X = idwt2(cA,cH,cV,cD,’bior1.1′)
% Black and White segmentation
BW=imquantize(X,level);
%% Iterative Canny Edge (Novel Method)
BW1 = edge(edge(BW,’canny’), ‘canny’);
%% Post-Processing
BW3 = imclearborder(BW1);
CC = bwconncomp(BW3);
S = regionprops(CC, ‘Area’);
L = labelmatrix(CC);
BW4 = ismember(L, find([S.Area] >= 100));
BW5 = imfill(BW4,’holes’);
%% Present Final Image[B,L,N] = bwboundaries(BW5);
figure; imshow(im); hold on;
for k=1:length(B),
boundary = B{k};
plot(boundary(:,2),…
boundary(:,1),’g’,’LineWidth’,2)