Medical Image Processing Projects

We support Medical image processing projects for researcher to implement proposed algorithm in matlab to retrieve accurate result. Matlab is the one best simulation tools for PhD researcher. We offer matlab for PhD projects under CSE, ECE and IT research scholars. We ensure matlab as friendly tool to develop image processing, neural network and digital signal processing. We published more than 80 PhD projects published in IEEE transaction. We support research scholar to select matlab PhD academic research paper from IEEE.


Medical Image Processing Projects:

Medical Image Processing concepts are developed under matlab simulation. Most of the Research scholars should take objective on medical imaging and also select corresponding modalities also. Modalities are CT, MRI, X-RAY, Ultrasonics and Microwave Tomography. Lots of processes included in medical image processing. Processes are Noise Removal, Morphological operations, Edge Enhancement, Segmentation, Feature Extraction, Feature Selection and Classification.


Detecting uncertainly in image forensics by fuzzy method:      We support and help PhD research scholars to do project in digital image forensics with authenticity and integrity of shared images. We propose various algorithms in matlab. We implement image forensics application require various tool to manipulate images. While using multiple tools in detection process, it arises uncertainly in error prone tools and merging of sound strategy to overcome this problem, we introduce decision fusion based fuzzy theory by our project developer.


Sclera vein recognition system:      We investigate sclera which is the white outer layer of human eye. The blood vessel structure of sclera is unique for every person which is used to is identify human. We introduce minutiae detection, speed up robust feature based method and direct correlation matching method in vein recognition projects. We implement parallel sclera with recognition method with two stage approaches for feature extraction and matching process. To retrieve feature information from sclera image we adopt rotation and scale invariant Y shape descriptor method, we employ weighted polar line scalar descriptor to accurately identify human.


Forest fire image recognition based on neural network:     We find a solution for complex process in fire detection system is finding large area and long distance feature of outer fire region. We offer neural network based fire detection system to diagnosis static and dynamic feature of fire. By this method, we build multiple parameters of framed image & distinguish fire image shape. To over complexity in fire detection system we adopt lateral extension. By this method we perform signal acquisition and signal processing methods enhance overall system performance.

Fungus analysis in tomato crop using image processing techniques:    We identify cercospora leaf spot or cercospora crucifier arum an types of fungus which affect the tomato leaf by modifying green color into brown, gray or off white and often kill young seeding. We propose machine vision system to visually inspect leaf coming from soil & determine fungus type and tomato stream depth.