Medical Image Processing Projects are created based on computerized imaging system. It should give the scanned image for processing and identification of disease presented in the particular medical images. Some uprising in medical images are in imaging technology has tissue development, Computer science and micro-electronics has some advancements, qualitative and quantitative diagnosis.Medical Image Processing Projects are developed based on image processing simulation tool named as Matlab.

     In medical imaging different types of imaging modalities are used. There are ultrasound, MRI, Hyper spectral imaging, 3D/4D imagers, vein viewer, Computed Tomography, PET, OCT and X-Ray.  For clinical purpose medical imaging techniques are used to generate human body internal images. Generally, a non-invasive technique will be used. At the time of surgery the different modalities can be used for getting the internal images.


Algorithm used in Medical Image Processing Projects

     Medical Image Processing Projects are developed based on image processing simulation tool named as Matlab. Using the tool processing more medical images of human organs are (Brain, Lung, Kidney, Skin, Retina, Finger, Tissues and Skull). According to the modality results the physician can easily observe the pathologies directly but sometimes it took more time to analyzing. Image analysis process can be automated for producing interesting results about human diseases.

           In medical image processing projects we have to use more algorithms to identify and classify the diseases in the images. Segmentation and classification methods are used to detect the disease and known the status of the human. Some of the commonly used classification algorithms are

Support Vector Machine.

Fuzzy C-Means Clustering.

K-NN Classification.

Naive Bayes Classification.

Decision Trees.

Genetic Algorithm.

Neural network Classification.

            Before enter into the process of classification we must do the processes of pre-processing, feature extraction and feature reduction. Pre-processing based on some grayscale conversion methods, noise removal concepts. Feature extraction is the process of extracting features in the images with its pixels. Most commonly extracted features are color, shape, texture, geometric features. In medical image processing greatly define the texture features. Then extracted features are to be reduced using feature reduction or selection method. The particular selected features are used to identify the disease in the human organ.

    Requirements for medical image processing are image enhancement, changing density range of B/W images, manipulating colors, image line profile display, image restoration, image smoothing, Biomedical image area calculation, detection of contour. Applying the algorithms of filtering and reconstruction automatically identify the 3D image data, it must take more number of object slices for processing.