Diaz-Pap smear Cell Image Classification Using Global MPEG ...

MotivationMethodsResults

Pap smear Cell Image Classification Using GlobalMPEG-7 Descriptors

Luz Helena CamargoGloria D́ıaz Ph.D

Eduardo Romero Ph.D

Luz H. Camargo, G. Diaz, E. Romero Pap smear Cell Image Classification Using Global MPEG-7 Descriptors


Contents

1 Motivation

2 Methods

3 Results



Contents

1 Motivation

2 Methods

3 Results



Worldwide Impact

Figura: Global cervical cancer incidence rates 100000 women (all ages),standardized according to standard population age of the OMS 2005

Globocane. 2002, 2008 (529.000 new cases-275.000 deaths)



Mortality Americas

Globocane. 2000- 2009, 1800deaths anually



Development of normal epithelium to invasive cancer

OMS. 2007



Classification system. Modified OMS 2007

Pap NIC OMS BethesdaClass I Normal Normal NormalClass II Atypia Atypia ASCClass III NIC I Dysplasia Leve LSILClass III NIC II Dysplasia moderate HSILClass III NIC III Dysplasia severe HSILClass IV NIC III Carcinoma in situ HSILClass V Carcinoma Carcinoma Carcinoma

invasor invasor invasor

OMS. 2007



The Problem

MotivationThe classification systems require segmentation: local adaptivesegmentation, seed growing, mathematical morphology, Hough transformadn active contours.

Cost: computational and economic development. Jantzen and Douniaspropose several cell features.



Contents

1 Motivation

2 Methods

3 Results



Classification based on Global MPEG-7 Descriptors

Descriptor

Color Layout Descriptor

Scalable Color Descriptor

Edge Histogram Descriptor



Color Layout Descriptor



Scalable Color Descriptor



Edge Histogram Descriptor



Classification Models

KNN: is a intuitive methos tha classifies unlabeled samplesbased on theri similarity whit samples in the training set.

SVM: is a classification model that finds an optimalseparating hyperplane that discriminates two classes.



Contents

1 Motivation

2 Methods

3 Results



Database

Clase B 1 B 2Normal 1. Superficial (50) 1. Superficial (74)

N. 2.Intermediate (50) 2.Intermediate (70)N. 3. Parabasal (50)N. 4. Columnar (50) 3. Columnar (98)

Total N. 200 242Anormal 5. Mild Dysplasia (100) 4. Mild Dysplasia (184)

A. 6. Moderate Dysplasia (100) 5. Moderate Dysplasia (146)A. 7. Severe Dysplasia (106) 6. Severe Displasia (197)A. 7. Carcinoma in situ (150)

Total A. 306 675Total 506 917



Database

OldDatabase



Database

NewDatabase



Results



Conclusions

This work presented an original method for discrimination of eachclass: normal and abnormal cell.



Thank you....


Diaz-Pap smear Cell Image Classification Using Global MPEG ...

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