cervical cancer classification using gabor filters 1026
TRANSCRIPT
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CERVICAL CANCER CLASSIFICATION USING
GABOR FILTERS
2011 First IEEE International Conference on Healthcare Informatics, Imaging and Systems
Biology
Advisor : Yin-Fu Huang
Student : Chen-Ju Lai
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OUTLINE
INTRODUCTION DATA COLLECTION METHODOLOGY AND RESULT CONCLUSION
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INTRODUCTION
Cervical cancer Biopsy test Cervical intraepithelial neoplasia (CIN)
Input : histology images Feature extraction : texture , using Gabor
filter Classification method : K-Means Clustering Output : Normal/CIN1/CIN2/CIN3/Malignant
Pre-cancer
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DATA COLLECTION
Pathology anatomy laboratory of Saiful Anwar hospital
Biopsy images : resolution 4080 x 3072 pixels
(categorized by an expert pathologist) 475 labelled images are used in this study
Normal CIN1 CIN2 CIN3 Malignant
60 70 50 50 245
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DATA COLLECTION
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CANCER GRADING
METHODOLOGYAND
RESULT
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GABOR FILTER
Gabor elementary function
2D Gaussian function
From (1) and (2), the Gabor elementary function can be rewritten as
Spatial domain
σx and σy are the spread of the Gaussian in x and y directions
centre frequency
x'=x cos θ +y sin θ and y'=-x sin θ+y cos θ.
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GABOR FILTER
Assuming σx and σy are the sameFrequency domain
U
V
θ
U0
φ
u
v
u'=u cos θ +v sin θ and v'=u sin θ+v cos θ
(U,V) can decision (U0 ,θ)
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GABOR FILTER
Sample
Original (a) f = 0.2,θ = 0 0 (b) f = 0.2,θ = 45 0
(c) f = 0.2,θ = 90 0 (d) f = 0.2,θ = 135 0
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COMPARE TEMPLATE
Compare each pixel with the templates. Supervised Training : generated templates
24 distinctive Gabor filters are used to generate a feature vector for each pixel and its neighbors.
background
basal stroma normalcells
abnormal cells
500 pixels 500 pixels
500 pixels
500 pixels
500 pixels
average average average average average
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SEGMENTED IMAGE & K-MEAN CLUSTERING
Segmentation After each pixel compare with the five feature
vector templates. blue : background , yellow : basal , white : stroma, green : normal cell , red : abnormal cell
K-Means Clustering Based on the color. Quantify the normal nuclei and abnormal nuclei.
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SEGMENTED IMAGE & K-MEAN CLUSTERING
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CALCULATE THE RATIO AND GRADING
How to classify the image into categories ? Use the ratio of number of normal and abnormal
cells.
Benign the number of abnormal cells < 5
CIN 1 ratio between abnormal and normal cells < 1/3
CIN 2 ratio between abnormal and normal cells between 1/3 ~ 2/3
CIN 3 ratio between abnormal and normal cells> 2/3 or full
Malignant
ratio between abnormal and normal cells > CIN 3
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CALCULATE THE RATIO AND GRADING
Table 1 shows the sample of the ratio between abnormal and normal cell.
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CALCULATE THE RATIO AND GRADING
Table 2 shows the confusion matrix of the Gabor filter hybrid with K-means clustering.
The sensitivity of normal is 87%, CIN 1 is 86%, CIN 2 82 %,CIN 3 84% and malignant is 89%.
The percentage of specificity of this system is 85%.
(52/60)=0.87(60/70)=0.86(41/50)=0.82(42/50)=0.84(219/245)=0.89
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COMPARED WITH SERVAL METHOD
Gray level Features , color K-mean and incremental thresholding.
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CONCLUSION
A methodology of Gabor filter bank with hybrid K-means clustering algorithm has been proposed.
Designing Gabor filter bank with the optimum selection parameters and different classification method can improve performance using this algorithm.