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Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution

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Abstract

For detection and classification of pulmonary nodules, there are two major issues exists in the existing computer aided diagnosis system. First major problem is automatic threshold to segment lungs and nodules. Threshold selection is a critical preprocessing step for medical images. Gaussian approximation based differential evolution has been used to find out the optimal threshold value for segmentation of lungs. Initially, 1-D histogram of the image is estimated using a blend of Gaussian functions whose parameters are calculated using the differential evolution method. Every Gaussian function estimating the histogram characterizes a pixel class and hence a threshold point. Second major problem is to extract the optimized features for classification of nodules. So, a novel gradient intensity feature descriptor for pulmonary nodule classification has been proposed using the multi-coordinate histogram of gradient and intensity based statistical features descriptor. Ensemble bagging trees has been used intelligently using the concepts of ensemble to classify the nodules. We have used standard dataset titled lung image consortium database for the verification and authentication of our proposed computer aided diagnostic (CAD) system. The proposed CAD system gives better results in comparison with existing CAD systems. The sensitivity of 97.5% is attained with an accuracy of 98.7%.

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Authors and Affiliations

  1. Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    M. Arfan Jaffar

  2. Foundation University, Islamabad, Pakistan

    Abdul Basit Siddiqui

  3. Forman Christian College, Lahore, Pakistan

    Mubashar Mushtaq

Authors
  1. M. Arfan Jaffar
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  2. Abdul Basit Siddiqui
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  3. Mubashar Mushtaq
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Correspondence to Abdul Basit Siddiqui.

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Jaffar, M.A., Siddiqui, A.B. & Mushtaq, M. Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution. Cluster Comput 21, 393–407 (2018). https://doi.org/10.1007/s10586-017-0876-6

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  • DOI: https://doi.org/10.1007/s10586-017-0876-6

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