Identifying Deviations in Microscopic Blood Images using Parallel Computing and Divide and Conquer Image Fragmentation
DOI:
https://doi.org//10.32628/CSEIT206225Keywords:
BFS, Deviations, Fragmentation, Divide & Conquer, Shortest Path Algorithm, Parallel Computing.Abstract
Most of the patient diagnosis revolves around in identifying abnormalities in their respective medical images. These images are of various types, likely Ultrasound, CT scan, MRI and microscopic images like bio-chemical slides, micro-biological slides & pathological slides. Few abnormalities are fractures, bad cells in blood, tumors, fungal identification etc. Finding the abnormal portions in these images needs expertise by the physician; this apt identification promotes and guarantees healthy medication by the physician or surgeon to patient. In medical microscopic images normal portions and abnormal portions are mixed together. None of the abnormal portions are related to abnormal and normal portions of image i.e. deviations are scattered among normal portions of image. These deviations are not present in some portions for specific area in the images. None of these deviations are overlapped nor can be grouped together into a single portion physically in the image. Deviations are isolated along with normal portions of images. Identifying such deviations is vital. In previous methods these deviations are identified used BFS and Shortest Path Algorithm. This paper focuses on identifying deviations using parallel computing applied over fragmented portions of blood images using divide and conquer.
References
- Sickle Cell Disease Symptoms, Causes,Treatments-Web MD.(n.d.).from https://www.emedicinehealth.com/sickle_cell_crisis/article_em.htm#what_is_sickle_cell_disease_scd.
- Siddharth Barpanda, (May-2013), “Use of Image Processing Techniques to Automatically Diagnose Sickle-Cell Anemia Present in Red Bloodan Cells Smear”from https://ethesis.nitrkl.ac.in/5022/1/109EE0255.pdf.
- "Classifying Deviations In Medical Microscopic Images Using Evolutionary Analysis" Iconic Research And Engineering Journals Volume 1 Issue 9 2018 Page 233-240 .
- "Clustering Deviations in Medical Images using Hierarchical Clustering and Shortest Path Algorithms" Iconic Research and Engineering Journals Volume 9 Issue 3
- Athira Sreekumar, prof Ashok Bhattacharya, “Identification of Sickle cells from Microscopic Blood Smear Image Using image processing” from https://www.academia.edu/24101001/Detection_of_Sickle_Cell_Anemia_in_Red_Blood_Cells_A_Review_ARTICLE_in_INTERNATIONAL_JOURNAL_OF_ADVANCES_IN_ENGINEERING_SCIENCES_AND_APPLIED_MATHEMATICS_MARCH_2015_READS_166.
- Types of Medical Images” https://www.ausmed.com/cpd/articles/medical-imaging-types-and-modalities.
- “Image Processing Functions in Matlab” https://www.mathworks.com/help/images/ref/regionprops.html
- I.A. Chintawar. Pravin N. Aishvarya M. Chetan K, (March 2016), “Detection of Sickle Cells using Image Processing”, IJSTE - International Journal of Science Technology & Engineering, Volume 2, Issue 09, ISSN (online): 2349-784X .
- Mojtaba T, Mona N, Behzad B, Alireza M, (January 2013), “New Approach to Red Blood Cell Classification Using Morphological Image Processing”, Shiraz EMedical Journal, Vol. 14, No. 1
- J. Poomcokrak and C. Neatpisarnvanit, “Red Blood Cells Extraction and Counting”, The 3rd International Symposium on Biomedical Engineering (ISBME 2008).
- Bacus J. W. and Weens J. H., “An automated method of differential red blood cell classification with application to the diagnosis of anemia”, J Histochem Cytochem, 25: 614, 1977.
- “Medical Imaging”, https://en.wikipedia.org/ wiki/ Medical_ imaging
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