Authors: Hemant Markhande, Prajwal Selokar, Nisha Raut, Ateef Ahmad, Vijay V Chakole
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This research creates a curiosity among people that how technology brings its value to the medical sector. This study is manly based on how Artificial Neural Network (ANN) technology collaborated with the MATLAB and gives magnificent results with the help of Digital X-rays. The project focuses on how technology is creating an ease to the medical sector while diagnosing a patient with a tumor. With this technology doctors can identify and help to cure the tumor at its initial stage. This technology benefited thousands of patients with their tumor. The method is contributing its intelligence to gives more accurate, faster and convenient results. The primary goal of our research is to make an algorithm which brings its value over the table in comparison with the older ones
Tumor can be defined as a morbid enlargement of body cells which results into abnormal growth of tissue and division of cells itself which turns in swelling of a particular organ. As, medical industry growing day by day. There are different kinds of tumors existing in the human body like brain tumor, bone tumor, lung tumor, etc. While classifying the tumor, image processing is important for analysis. The Outcomes of medical image processing is used to cure the health issues. Bone tumors develop when cells within a bone divide uncontrollably, forming a lump or bloat of abnormal tissue. There are two types of tumors, Benign (Non-cancerous) and Malignant (Cancerous). In our project we are mainly fascinated about image segmentation for a bone image and their classification. In this process input was segmented and the features are extracted in the first module, in the second module the images are classified as Benign or malignant with the use of SVM (Support Vector Machine) and ANN (Artificial Neural Network) classifiers. This project proposed a simple and easy method to detect and classify the bone tumor. X-rays are important medical tool for doctors because x-ray releases the type of radiation called electromagnetic waves, which helps creating pictures of an inner part of the human body in different shades of gray. Digital radiography is widely known as digital x-ray. Digital x-ray images are clearer and help to overcome errors faced by older x-ray technology and that too with quick output, which is helpful for proper diagnosis. This project is to make a digital x-ray based, image processing system which gives a fast and accurate identification of the disease supported by the data gained from the digital x-ray images that are stored in a computer have format like jpg, png etc.
A. Morphological Operation
The features of an image processing in the shape or morphology are related with the collection of non-linear operations is called as morphological operation in image processing. Morphological operations create an output image of the same size by applying a structure element to an input image. In a morphological operation, the value of each pixel in the input image with its neighbours is based on a differentiation by corelating with the pixel in the output image. You can construct a morphological operation by selecting the shape and the size of the neighbourhood which is sensitive to certain formation in the input image. The most used morphological operations are dilation and erosion. The morphological operation in an image, erosion clear pixels from object boundaries, whereas the dilation add pixels in the boundaries of an object. The number of pixels added or erased from the objects in an image depending on size, shape of the structured element used to process the image. In the morphological operations erosion as well as dilation, the state of any given pixel in the input image and corresponding pixel are ruled by applying the determined output image. Here, the rule is used to process the pixels define the operation as the erosion or dilation discussed about effective content-based medical image retrieval, an erosion operation is used to shrink or eliminate small objects and command used in MATLAB is the imerode. Dilation operation is used to expand regions and edges and the command used in MATLAB is the imdilate.
Dilation added pixels in the binary image. There are other types that work on different shades of black and white images. The essential result of the operator on a binary image, is to bit by bit enlarge the boundaries of regions of foreground pixels. Thus, area of the foreground pixels grows in size whereas holes among those regions become smaller. The dilation takes two pieces of data as an input. The initial image which is to be dilated and the second is a set of coordinate points referred to as a kernel.
C. Proposed Work
D. Block Diagram
a. Importing the image using optical scanner or digital radiography.
b. Analyzing and manipulating the image which have data compression, image enhancement and spotting patterns that are not seen by human eye like satellite image.
c. Output is the final stage in which result is improved picture or report i.e., based on image analysis.
This proposed system of bone tumor detection with super pixel segmentation is carried out with MATLAB. Also, the detection of brain cancer is carried out with the given set of images. The proposed system is especially dedicated for brain tumor detection. The same system can be further extended to identifying the stages of cancer. In this project we have proposed and plan an exceptional strategy for the location of the bone tumor for various modalities. Our work will be reached out for structuring tumor acknowledged with the mean to decrease the computational time and cost. This strategy gives a sophisticated approach to picture handling operation with examination to find the bone tumor in human body.
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