Brain Tumour Segmentation using SFCM & CNN | Matlab


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The identification of objects in an image would probably start with image processing techniques such as noise removal, followed by (low-level) feature extraction to locate lines, regions, and possibly areas with certain textures.

The clever bit is to interpret collections of these shapes as single objects, e.g. cars on a road, boxes on a conveyor belt or cancerous cells on a microscope slide. One reason this is an AI problem is that an object can appear very different when viewed from different angles or under different lighting. Another problem is deciding what features belong to what object and which are background or shadows etc. The human visual system performs these tasks mostly unconsciously but a computer requires skillful programming and lots of processing power to approach human performance. Manipulating data in the form of an image through several possible techniques. An image is usually interpreted as a two-dimensional array of brightness values and is most familiarly represented by such patterns as those of a photographic print, slide, television screen, or movie screen. An image can be processed optically or digitally with a computer.

To digitally process an image, it is first necessary to reduce the image to a series of numbers that can be manipulated by the computer. Each number representing the brightness value of the image at a particular location is called a picture element, or pixel. A typical digitized image may have 512 ? 512 or roughly 250,000 pixels, although much larger images are becoming common. Once the image has been digitized, there are three basic operations that can be performed on it in the computer. For a point operation, a pixel value in the output image depends on a single-pixel value in the input image. For local operations, several neighbouring pixels in the input image determine the value of an output image pixel. In a global operation, all of the input image pixels contribute to an output image pixel value.

Existing method

  • Partial derivatives
  • Wavelet-based denoising
  • Threshold and K means clustering methods for segmentation


  • Loss of edge details
  • In wavelet denoising, failure to detect edge details at the curved region.
  • K means – It is not suitable for all lighting condition of images
    • Difficult to measure the cluster quality


  • The Project proposes to spot the tumor from MRI scanned medical images using multi clustering model and morphological process.
  • Segmentation refers to the process of partitioning a digital image into multiple segments.
  • The brain MRI is taken and its noises are removed using filters and then applied spatial Fuzzy C means Clustering algorithm for the segmentation of MRI brain images.
  • The morphological process will be used to smooth the tumor region from the noisy background.
  • The segmented primary and secondary regions are compressed with hybrid techniques for telemedicine application.

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