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Lesion detection in mammogram

Based on Multiresolution Analysis

Under the guidance of,Ms. S .Deivalakshmi

Presented by,

Sujay Pujari.

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Introduction to Mammogram

Problem statement

Preprocessing Segmentation

References

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Introduction to Mammogram A mammogram is an x-ray image of the breast, used to detect

and diagnose breast diseases.

Two basic views of mammographic image:

(a) craniocaudal (CC) view

(b) Mediolateral oblique (MLO) view

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To detect and classify Lesion without humanintervention

To use adaptive Threshold segmentation (local and

global)

To explore Multiresolution analysis of wavelettransform

To explore suitable wavelet transform and scale for

optimum result.

Problem statement:

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ROI (Lesion)

Lesion

Classification

Typical steps in Biomedical

image processing algorithms

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Preprocessing

To remove Label

(skin line detection)

Left sided or Right sided MLO view To remove rib portion from mammogram

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SegmentationSegmentation

.1 Thresholding Techniques

Local thresholding

Local adaptive thresholding

Global thresholding

Multiresolution method.

.4 Hybrid Techniques

.2 Region-Based Techniques

.3 Edge Detection Techniques

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Multiresolution analysis

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Implemented algorithm (part1)

Preprocessed Mammogram

Scale 1,LL channel

Histogram

Smoothing(moving average)

second derivative

Threshold=first Zero crossing

Im2Bw

Mask

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Ref

By Jelena Bozek, Mario Mustra, Kresimir Delac, and Mislav Grgic ,A Survey ofImage

Processing Algorithms in Digital Mammography, Sig. Process. and Commun., SCI231, pp. 631657.springerlink 2009

Kai Hu, Xieping Gao and Fei Li Detection of Suspicious Lesions by Adaptive

Thresholding Based on Multiresolution Analysis in MammogramsIEEE

TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, apr-2010

Mammogramdatabases:

http://cobweb.ecn.purdue.edu/~ace/mammo/mammo_

db.html

Introduction to Wavelets and Wavelet Transforms, A Primer

PH 1997 - C Sidney Burrus, Ramesh A Gopinath, Haitao Guo

X. P. Zhang and M. D. Desai, Segmentation of bright targets using

wavelets and adaptive thresholding, IEEE Trans. Image Process., vol. 10,

no. 7, pp. 10201030, Jul. 2001.

G. Kom, A. Tiedeu, and M. Kom, Automated detection of masses

in mammograms by local adaptive thresholding, Comput. Biol. Med.,

vol. 37, no. 1, pp. 3748, Jan. 2007.

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Thank

You