Content based Image Retrieval (CBIR) System for leukemic diseases using MATLAB

1. I need a Content based Image Retrieval (CBIR) System for leukemic diseases (e.g. ALL, AML, CML, CLL), would love if you could create milestones and delivery times for each, but no more than three weeks (21 days) for the whole project. 2. The program shall take several images (blood cells images) A link to the images dataset will be sent later, an example image is attached. 3. The program shall use a database that has blood cells images, on adding a new image, we will enter the patients' information (first name, last name, age ,country ,city ,email address, phone number) to make a record and it shall compare the image with others in the Database, classify it, and then give the similarity. 4. for Segmentation, you should use 4 different methods: a. K-means algorithm b. Edge detection using histogram equalizing method and linear contrast stretching c. Watershed transform d. Global contrast enhancement technique (GCs) and segmentation based on HSI (Hue, Saturation, Intensity) color space. some of those are already implemented before in other research papers. 5. for Feature extraction, you can use: a. Texture features (such as energy, contrast, homogeneity, correlations and Hausdorff Dimension (HD) b. Shape-based features like (major axis, minor axis, area, perimeter, standard deviation, radius, roundness) c. Area of object, parameter of object, Fourier descriptor, and local binary pattern (LBP). 6. For classification, you can use French-American-British (FAB) classification or use the "Support Vector Machine" classification technique (Typically Euclidean distances). Read more: [login to view URL] 7. For the research purpose of the program, it shall give a statistical chart that shows: a. how many persons has this disease (filtered by the last name). b. what is the percentage of this kind of cancer in a city. c. the average age of the patients. 8. Also the program shall send an email to the patient to inform them about the result (Leukemia type AML, ALL, CCL, CML) with brief information about the disease. 9. Use good programming practices, including indentation, commenting your code and choosing meaningful variable names to make your functions self-documenting, and make sure that your code works properly and is free of errors by utilizing the resources at your disposal.