Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2013  |  Volume : 4  |  Issue : 2  |  Page : 8

Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions

1 Department of Computer Science, IIT Bombay, India; Department of Biomedical Engineering, Case Western Reserve University, USA
2 Department of Computer Science, IIT Bombay, India
3 Department of Biomedical Engineering, Case Western Reserve University, USA

Correspondence Address:
Anant Madabhushi
Department of Biomedical Engineering, Case Western Reserve University, USA

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2153-3539.109865

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Introduction: The notion of local scale was introduced to characterize varying levels of image detail so that localized image processing tasks could be performed while simultaneously yielding a globally optimal result. In this paper, we have presented the methodological framework for a novel locally adaptive scale definition, morphologic scale (MS), which is different from extant local scale definitions in that it attempts to characterize local heterogeneity as opposed to local homogeneity. Methods: At every point of interest, the MS is determined as a series of radial paths extending outward in the direction of least resistance, navigating around obstructions. Each pixel can then be directly compared to other points of interest via a rotationally invariant quantitative feature descriptor, determined by the application of Fourier descriptors to the collection of these paths. Results: Our goal is to distinguish tumor and stromal tissue classes in the context of four different digitized pathology datasets: prostate tissue microarrays (TMAs) stained with hematoxylin and eosin (HE) (44 images) and TMAs stained with only hematoxylin (H) (44 images), slide mounts of ovarian H (60 images), and HE breast cancer (51 images) histology images. Classification performance over 50 cross-validation runs using a Bayesian classifier produced mean areas under the curve of 0.88 ± 0.01 (prostate HE), 0.87 ± 0.02 (prostate H), 0.88 ± 0.01 (ovarian H), and 0.80 ± 0.01 (breast HE). Conclusion: For each dataset listed in [Table 3], we randomly selected 100 points per image, and using the procedure described in Experiment 1, we attempted to separate them as belonging to stroma or epithelium.

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