2-SiMDoM: A 2-Sieve model for detection of mitosis in multispectral breast cancer imagery

In this paper, we propose a 2-Sieve model for the detection of mitosis in breast cancer multispectral images. Multiresolution wavelet features & Gray Level Entropy Matrix (GLEM) features have been computed for each candidate on all the spectral bands. A novel dimensionality selection algorithm h...

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Bibliographic Details
Published in2013 IEEE International Conference on Image Processing pp. 611 - 615
Main Authors Tripathi, Ardhendu Shekhar, Mathur, Atin, Daga, Mohit, Kuse, Manohar, Au, Oscar C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2013
Subjects
Accuracy
Active contours
Breast cancer
Data Imbalance
Dimensionality Selection
Entropy
Image resolution
Mitosis Detection
Sensitivity
Training
Wavelet Texture
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Summary:In this paper, we propose a 2-Sieve model for the detection of mitosis in breast cancer multispectral images. Multiresolution wavelet features & Gray Level Entropy Matrix (GLEM) features have been computed for each candidate on all the spectral bands. A novel dimensionality selection algorithm has been introduced and its performance compared with other existing algorithms. Data imbalance and data cleaning have been taken care of using classical data mining techniques. Furthermore, a Second Sieve classification is performed to increase the Positive Predictive Value (PPV) with minimal loss in Sensitivity. A final Sensitivity and PPV of 82.35% & 73.04% respectively was achieved over the testing set using the proposed scheme.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2013.6738126