Extraction and classification texture of inflammatory cells and nuclei in normal Pap Smear


The presence of inflammatory cells complicates the
process of identifying the nuclei in the early detection of cervical
cancer. Inflammatory cells need to be eliminated to assist
pathologists in reading Pap smear slides. The texture of GreyLevel Run-Length Matrix (GLRLM) for inflammatory cells and
nuclei types are investigated. The inflammatory cells and nuclei
have different texture, and it can be used to differentiate them. To
extract all of the features, firstly manual cropping of
inflammatory cells and nuclei needs to be done. All of extracted
features have been analyzed and selected by Decision Tree
classifier (J48). Originally there have been eleven features in the
direction of 135º which are extracted to classify cropping cells into
inflammatory cells and nuclei. Then the eleven features are
reduced into eight, namely low gray level run emphasis, gray level
non uniformity, run length non-uniformity, long run low graylevel emphasis, short run high gray-level emphasis, short run low
gray-level emphasis, long run high gray-level emphasis and run
percentage based on the rule of classification. This experiment is
applied into 957 cells which were from 50 images. The
compositions of these cells were 122 cells of nuclei and 837 cells of
inflammatory. The proposed algorithm applied to all of the cells
and the result of classification by using these eight texture features
obtains the sensitivity rates which show that there are still nuclei
of cells that were considered as inflammatory cells. It was in
accordance with the conditions of the difficulties faced by the
pathologist while the specificity rate suggests that inflammatory
cells detected properly and few inflammatory cells are considered
as nucleus.

Kata Kunci: Classification, Cells Pap Smear, inflammatory cells, Texture, GLRLM, Decision Tree.


Bidang ilmu
Image Processing


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