Abstract
Recent year’s witnessed a huge revolution for developing an automated diagnosis for different disease such as cancer using medical image processing. Many researches have been dedicated to achieve this goal. Analyzing medical microscopic histology images provide us with large information about the status of patient and the progress of diseases, help to determine if the tissue have any pathological changes. Automation of the diagnosis of these images will lead to better, faster and enhanced diagnosis for different hematological and histological tissue images such as cancer. This paper propose an automated methodology for analyzing cancer histology and hematology microscopic images to detect leukemia using image processing by combining two diagnosis procedures initial and advance; the initial diagnosis depend on the percentage of the white blood cells in microscopic images affected by leukemia as indicator for the existence of leukemia in the blood smear sample. Whereas, the advance diagnosis classifying the leukemia according into different types using feature bag classifier. The experimental results showed that the proposed methodology initial diagnosis is able to detect leukemia images and differentiate it from samples that do not have leukemia. While, advance diagnosis it is able to detect and classify most leukemia types and differentiate between acute and chronic, but in some cases in the chronic leukemia where the percent of blast cells and shape are similar; it gave a diagnosis of the type of leukemia to the most similar type.
Highlights
Many novel researches and efforts have been devoted for developing automated systems for detecting and analyzing of microscopic histology images
Diagnosis traditionally depends on the qualified eye of a pathologist to make judgment from a qualitative perspective. computer automation and diagnosis is possible with digital image processing (Gonzalez and Woods, 2002)
The preprocessing is an essential phase in microscopic histology image analysis because of the nature and circumstances of the slides preparation, staining and image shooting, which affect the quality of the image as seen in Figure 3 and Figure 4 which shows poor quality images for both normal and leukemia images respectively
Summary
Many novel researches and efforts have been devoted for developing automated systems for detecting and analyzing of microscopic histology images. Computer automation and diagnosis is possible with digital image processing (Gonzalez and Woods, 2002). Digital image processing means the process of images by digital computer This includes detection, sensing, analysis of digital images (Jensen, 1996) (Alhadidi et al, 2006). Which contains limited number of elements named as pixels; these elements have values that represent image (Gonzalez and Woods, 2002). It begins with image acquisition and image enhancement, for the reason that irrelevant details shall be shown and potential highlight parts of details or interest features must be displayed. A lot of digital image steps applied to digital image such as object classification, segmentation, morphological processing ...etc. (Jensen, 1996) (Ablameyko and Nedzved, 2005) (Alhadidi et al, 2007)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
