Background: I present our medical context with some basic concepts in order to understand the results of our work, and then I begin the explanation of mathematical morphology. I will conclude by the description of algorithmic processing propose in this paper. Cancers, including leukemia and lymphoma, can cause uncontrolled growth of an abnormal type of blood cell in the bone marrow, resulting in a greatly increased risk for infection and or serious bleeding. Methods: We present detailed steps of our proposed systems, to obtain a final result that shows the detection of abnormal cells. It typically starts with a median filter pre-processing step and then applies different morphologic operator, which allows us to segment the original image and detect cancerous cells. The basic idea behind all the operators in the mathematical morphology is to compare the set of objects to analyze another object of known form, which is called a structuring element. The structuring element is a geometric figure, simple to form, known or arbitrary, and can be a circle, segment, square, or triangle. Results: We show the different results obtained after testing carried out in algorithmic processing using MATLAB: To ameliorate the visualization of the abnormal blood cells, we have applied the elements basis morphological operations in a different way. We have performed an opening by reconstruction and a closing by reconstruction. The obtained result show that we have obtained an efficient detection of the targeted objects (abnormal blood cells or leukemia). Conclusion: In this paper, we have utilized the operators of the mathematical morphology with the aim to detect abnormal cells for diagnostic aid and transmission of accurate and precise clinical information, which helps specialists in medicine (hematologists) to distinguish abnormal cells or cancerous and to follow the evolution of leukemia. The algorithmic processing presented in this article has been able to perform the task of detection of cancerous cells with success; it has produced remarkable and satisfactory results. We think of the future concept as a system of aid for diagnosis from microelectronics integration to the base of reconfigurable technologies applied to cells for the goal of quantification of the cancer region.
The field of biomedical diagnosis has become very important in the evolution of medicine, and especially for the detection of cancer cells. In this paper, we will develop an algorithmic treatment by segmentation, using elements of mathematical morphology. The aim of our work is to obtain a maximum rate of recognition of leukemia. Our algorithmic diagram can segment, detect, characterize and describe cancerous blood cells (leukemia). This proposal is an important task in the interpretation and diagnosis of pathologies for hematologists. The experimental results applied on our microscopic medical images are encouraging for the identification of abnormal blood cells generally, and the distinction of leukemia especially, which shows the efficiency and rapidity of our algorithmic system.
Leukaemia is a cancer of the hematopoietic cells. The detection of abnormal blood cells before cancer degeneration is a medical problem. The aim of our work is to obtain maximum recognition rate of leukaemia. We propose the development of a system based on mathematical morphology and k-means methods capable of segmentation, classification, and detection of the cancerous blood cells. This allows the characterisation and the description the cancerous region, which is an important task in the interpretation and diagnosis of pathologies present in blood. The segmentation was carried out using an efficient and fast algorithmic processing. It turns out that the proposed system shows to better segmentation and classification for tested images. The obtained experimental results are very encouraging which help hematologists for identification of abnormal blood cells.
The aim of our work is to obtain a maximum rate of recognition of abnormal (cancerous) blood cells. We propose the development of a system based on kmeans methods, after an RGB channel decomposition by applying the algorithm which can segment our microscopic medical images. It turns out that the proposed system shows better segmentation and classification for the identification and detection of leukemia. The experimental results obtained are very encouraging, which helps hematologists to monitor the evolution of cancerous blood cells and make a good diagnosis.
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