Design of an automated slide capturing system with a dsp-based automatic features extraction of thinprep® images for cervical cancer
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Date
2008-07
Authors
Mat Noor, Nor Rizuan
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Abstract
Conventional method of cervical cancer screening involves of pathologist or cytologist
examining the Thin Prep® cervical smear slide under normal light microscope with 1 OOX
and 400X magnification. ThinPrep® is a technology from Cytyc Corporation that has
been used widely for producing mono-layer and clean cervical smear slide. This study
focuses on developing an automated ThinPrep® slide capturing system and a DSPbased
automatic features extraction system for the ThinPrep® images. The automated
slide capturing system consists of automated microscope, digital camera and personal
computer that work together to handle the slide in order to acquire it into digital form.
The capturing process is carried out under 1 OOX and 400X magnification as preferred
by pathologist and cytologist. Image retrieval or displaying, facilities are embedded
inside the system that capable to display the captured images in various ways. The
images could be displayed in capturing order, most suspected images, 1 OOX-400X
magnification map and also capable to display the cell that partially displayed in one
image. The DSP-based automatic features extraction system on the other hand
capable to extract certain features from the ThinPrep® images. The system is based on
digital signal processor (DSP) from Texas Instruments, TMS320C6416, a fixed-point
DSP. The features that need to be extracted are average grey level, size and perimeter
for both of nucleus and cytoplasm of the cervical cell. The features are needed by the
pathologist and cytologist to determine the pre-cancerous level of the cells. The
extraction process starts with clustering technique that uses moving k-mean (MKM)
algorithm to find the threshold value to differentiate nucleus, cytoplasm and
background of the cervical cell. Invariant moment technique is used to find the centroid
location of the cell's nucleus. Starting from the centroid location, seed-based region
growing (SBRG) algorithm is used to segment and at the same time extracts all the
features from both of nucleus and cytoplasm of the cell. All of these steps are from
automatic features extraction (AFE) algorithm that have been used for extracting
features from cervical cell image on a personal computer. In the current study, the AFE
algorithm will be implemented inside the DSP and some code optimization techniques
will be used to fully utilize all local resources inside the DSP that are limited as
compared to the personal computer. The AFE algorithm inside the DSP and personal
computer could process the ThinPrep® image in 0.4640 and 0.1048 milliseconds per
unit pixel, respectively. Both of the automated slide capturing system and the DSPbased
automatic features extraction system proposed the new ways of examining and
screening the ThinPrep® slide.
Description
Keywords
Automated slide capturing system , Features extraction , Thinprep® images , Cervical cancer