An image analysis approach for automatic malignancy determination of prostate pathological images

Abstract

Background: Determining malignancy of prostate pathological samples is important for treatment planning of prostate cancer. Traditionally, this is performed by expert pathologists who evaluate the structure of prostate glands in the biopsy samples. However, this is a subjective task due to inter-and intraobserver differences among pathologists. Also, it is time-consuming and difficult to some extent. Therefore, automatic determination of malignancy of prostate pathological samples is of interest.Methods: A texture-based technique is first used to segment the prostate glands in the image. Features related to size and shape of these glands are then extracted and combined to generate an index, which is proportional to malignancy of cancer. A linear classifier is employed to classify the specimens into benign (low potential for malignancy) and malignant.Results: The leave-one-out technique is employed to evaluate the method using two datasets. The first has 91 images with similar magnifications and illuminations while the second has 199 images with different magnifications and illuminations. In the experiments, accuracies of about 98 and 95% have been achieved for these two datasets, respectively.Conclusions: An image analysis approach is employed to evaluate prostate pathological images. Experimental results show that the proposed method can successfully classify the prostate biopsy samples into benign and malignant. They also show that the proposed method is robust to variations in magnification and illumination. q

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

“…Accuracy of the model is stable over much of the parameter range, with lower accuracy only occurring where lambda forces few to no features to be included in the model. Dundar et al, 2011Dundar et al, , 2010Esgiar et al, 2002;Farjam et al, 2007;Fatima et al, 2014;Glotsos et al, 2008;Gunduz et al, 2004;Gupta et al, 2001;Hall et al, 2008;Huang and Lai, 2010;Huang and Lee, 2009;Jafari-Khouzani and Soltanian-Zadeh, 2003;Monaco and Tomaszewski, 2008;Kong et al, 2007;Kwak et al, 2011;Land et al, 2008;Lessmann et al, 2007;Meng et al, 2010;Naik et al, 2008Naik et al, , 2007Ozolek et al, 2014;Petushi et al, 2006;Qureshi et al, 2008Qureshi et al, , 2007Sboner et al, 2003;Schnorrenberg et al, 1997;Sertel et al, 2010;Sparks and Madabhushi, 2013;Sudbø et al, 2000;Tabesh and Teverovskiy, 2006;Tabesh et al, 2007Tabesh et al, , 2005Tahir and Bouridane, 2006;Tasoulis et al, 2003;Teverovskiy et al, 2004;Thiran and Macq, 1996;Tsai et al, 2009;Xu et al 2014aXu et al , 2014bYang et al, 2009). These approache...…”

“…Many other methods collect features from either the original RGB image, a converted image to other non-biologically based color spaces (e.g., Lab or HSL), or from a grayscale version of that same image (Al-Kadi, 2010;Basavanhally et al, 2010;Dundar et al, 2011Dundar et al, , 2010Esgiar et al, 2002;Farjam et al, 2007;Glotsos et al, 2008;Huang and Lee, 2009;Jafari-Khouzani and SoltanianZadeh, 2003;Kong et al, 2009;Ozolek et al, 2014;Petushi et al, 2006;Qureshi et al, 2008;Ruiz et al, 2007;Schnorrenberg et al, 1997;Tabesh et al, 2005;Tabesh and Teverovskiy, 2006;Tahir and Bouridane, 2006;Thiran and Macq, 1996;Tuzel et al, 2007;Wang et al, 2010;Wetzel et al, 1999;Weyn et al, 1998;Xu et al 2014aXu et al , 2014b. Since hematoxylin binds to nucleic acids and eosin binds to protein, unmixing the stains allows the feature extraction to directly probe the state of these important biological molecules, whereas features from the mixed image may either miss this signal or be unable to probe them independently.…”

“…Even before WSI was practical, researchers captured a single frame of an entire slide using a standard microscope creating a subset of the image for computational analysis (Adiga et al, 2006;Aiad et al, 2009;Altunbay et al, 2010;Baak et al, 1981;Brook et al, 2007;Esgiar et al, 2002Esgiar et al, , 2002Farjam et al, 2007;Fatima et al, 2014;Glotsos et al, 2008;Gunduz et al, 2004;Gupta et al, 2001;Hall et al, 2008;Huang and Lai, 2010;Huang and Lee, 2009;Jafari-Khouzani and Soltanian-Zadeh, 2003;Kong et al, 2007;Land et al, 2008;Lessmann et al, 2007;Meng et al, 2010;Ozolek et al, 2014;Qureshi et al, 2008Qureshi et al, , 2007Sboner et al, 2003;Schnorrenberg et al, 1997;Sudbø et al, 2000;Tahir and Bouridane, 2006;Tasoulis et al, 2003;Thiran and Macq, 1996;Tsai et al, 2009). For many researchers doing pathology image analysis today, this is still the best option, either because they do not have access to a WSI system, or because they can capture data not possible with a WSI system, such as confocal microscopy images.…”

GlandVision: A Novel Polar Space Random Field Model for Glandular Biological Structure Detection

“…Lumen is the region in the center of a gland. Previous methods assume that lumen can be identified by their color [20,21] or texture [7,10]. Once the lumen area is detected, it is considered as the seed region, and some segmentation methods, such as region growing [23], active contour [25], level-set [19], etc, can be adopted to segment out the glands.…”

Cytomics and nanobioengineering

“…Using wide view and deconvolution function the system is able to acquire and quantitate cells in tissue microarrays. This system was successfully applied in estimation of telomeres length in lung cancer lines as well as for lung cancer detection in bronchial alveolar lavage (BAL) (65,66).…”