A novel online Variance Based Instance Selection (VBIS) method for efficient atypicality detection in chest radiographs

Mohammad Alzubaidi; Vineeth Balasubramanian; Ameet Patel; Sethuraman Panchanathan; John A. Black Jr.

doi:10.1117/12.911154

24 February 2012 A novel online Variance Based Instance Selection (VBIS) method for efficient atypicality detection in chest radiographs

Mohammad Alzubaidi, Vineeth Balasubramanian, Ameet Patel, Sethuraman Panchanathan, John A. Black Jr.

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83144Z (2012) https://doi.org/10.1117/12.911154
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Chest radiographs are complex, heterogeneous medical images that depict many different types of tissues, and many different types of abnormalities. A radiologist develops a sense of what visual textures are typical for each anatomic region within chest radiographs by viewing a large set of "normal" radiographs over a period of years. As a result, an expert radiologist is able to readily detect atypical features. In our previous research, we modeled this type of learning by (1) collecting a large set of "normal" chest radiographs, (2) extracting local textural and contour features from anatomical regions within these radiographs, in the form of high-dimensional feature vectors, (3) using a distance-based transductive machine learning method to learn what it typical for each anatomical region, and (4) computing atypicality scores for the anatomical regions in test radiographs. That research demonstrated that the transductive One-Nearest-Neighbor (1NN) method was effective for identifying atypical regions in chest radiographs. However, the large set of training instances (and the need to compute a distance to each of these instances in a high dimensional space) made the transductive method computationally expensive. This paper discusses a novel online Variance Based Instance Selection (VBIS) method for use with the Nearest Neighbor classifier, that (1) substantially reduced the computational cost of the transductive 1NN method, while maintaining a high level of effectiveness in identifying regions of chest radiographs with atypical content, and (2) allowed the incremental incorporation of training data from new informative chest radiographs as they are encountered in day-to-day clinical work.

Citation Download Citation

Mohammad Alzubaidi, Vineeth Balasubramanian, Ameet Patel, Sethuraman Panchanathan, and John A. Black Jr. "A novel online Variance Based Instance Selection (VBIS) method for efficient atypicality detection in chest radiographs", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83144Z (24 February 2012); https://doi.org/10.1117/12.911154

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KEYWORDS

Chest imaging

Radiography

Machine learning

Feature extraction

Binary data

Medical imaging

Prototyping

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