Matlab is used to simulate the structure of images, morphological function and biological entities in images. We offer M.Tech projects in matlab simulation for academic ensure efficient feature selection, classification & segmentation algorithm for remote sensing images, medical image and digital image processing environments. Most medical images are in the form of ultrasound, x-ray, and CT and magnetic resonance images. We permit matlab for all medical images and ensure image processing, analysis, virtualization pattern recognition and image classification method. We develop M.Tech projects in matlab simulation for computer engineering, information technology and bio medical engineering students & researchers.

Remote sensing image processing in matlab:

Knowledge based classification for urban mapping:

We implement optimal classification process from ACM papers. To achieve the high performance in urban image classification process we integrate knowledge based approach with spectral classification method. We provide image processing which use hybrid technology and produce improved performance output.

Partially supervised classification method in matlab:

We classify remote sensed image based on classes and datasets. We provide map image for detail description about all the land cover types in image & need complete training set with time representation we determine Bayesian with semi supervised support vector machine to classify unknown class images. We estimate probability density function to achieve image processing.

Pattern extracting in remote sensing images:

Extraction of image features or patterns which is a complex problem in multi spectral image classification system we start with pattern recognition. More than 100+ projects are developed by introducing pattern selection algorithm, random selection algorithm and human interpreted selection method for searching spectral, spatial & temporal data from large scale data base. We achieve near optimal solution by implementing neural network algorithm pattern selection process.

Fuzzy input & fuzzy output support vector machine classifier in matlab:

We use fuzzy to generate input & obtain information. Then the information is given as input for classification algorithm. We use classifier for pattern and related information for addressing multiclass problem in fuzzy framework.

Medical image processing in matlab:

We adopt matlab in medical image classification segmentation process. By matlab we determine lung cancer segmentation, brain tumor segmentation, prostate cancer classification and kidney disease classification are done under M.Tech with help of matlab simulation tool.

Cardiac vascular disease segmentation in matlab projects:

We use transformed component analysis algorithm to estimate shape, variation & position in affected area in 2D MRI images. It generate statistical local appearance feature & construct accurate boundary region based on boosting methods. We practice transformed component analysis to represent low dimensional shapes in MRI images.