title: Texture analysis applied to second harmonic generation image data for ovarian cancer classification.
abstract: Remodeling of the extracellular matrix has been implicated in ovarian cancer. To quantitate the remodeling, we implement a form of texture analysis to delineate the collagen fibrillar morphology observed in second harmonic generation microscopy images of human normal and high grade malignant ovarian tissues. In the learning stage, a dictionary of “textons”—frequently occurring texture features that are identified by measuring the image response to a filter bank of various shapes, sizes, and orientations—is created. By calculating a representative model based on the texton distribution for each tissue type using a training set of respective second harmonic generation images, we then perform classification between images of normal and high grade malignant ovarian tissues. By optimizing the number of textons and nearest neighbors, we achieved classification accuracy up to 97% based on the area under receiver operating characteristic curves (true positives versus false positives). The local analysis algorithm is a more general method to probe rapidly changing fibrillar morphologies than global analyses such as FFT. It is also more versatile than other texture approaches as the filter bank can be highly tailored to specific applications (e.g., different disease states) by creating customized libraries based on common image features.
authors: University of Wisconsin-Madison, Department of Medical Physics, Madison, Wisconsin 53706, United StatesbMorgridge Institute for Research, Madison, Wisconsin 53715, United States.
authors: Bruce L
authors: BL
authors: Wen
authors: University of Connecticut Health Center, Department of Obstetrics and Gynecology, Farmington, Connecticut 06030, United States.
authors: Molly A
authors: MA
authors: Brewer
authors: VU University Amsterdam, VU Medical Center, 1007 MB Amsterdam, Netherlands.
authors: Oleg
authors: O
authors: Nadiarnykh
authors: University of Wisconsin-Madison, Department of Biomedical Engineering, Madison, Wisconsin 53706, United States.
authors: James
authors: J
authors: Hocker
authors: University of Wisconsin-Madison, Department of Biostatistics and Medical Informatics, Madison, Wisconsin 53706, United States.
authors: Vikas
authors: V
authors: Singh
authors: University of Wisconsin-Madison, Department of Medical Physics, Madison, Wisconsin 53706, United StatesbMorgridge Institute for Research, Madison, Wisconsin 53715, United States.
authors: Thomas R
authors: TR
authors: Mackie
authors: University of Wisconsin-Madison, Department of Medical Physics, Madison, Wisconsin 53706, United StateseUniversity of Wisconsin-Madison, Department of Biomedical Engineering, Madison, Wisconsin 53706, United States.
authors: Paul J
authors: PJ
authors: Campagnola
mesh_terms: D000465:Algorithms; D005109:Extracellular Matrix / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D010051:Ovarian Neoplasms / Q000145:classification* / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity