title: Frequential versus spatial colour textons for breast TMA classification.
abstract: Advances in digital pathology are generating huge volumes of whole slide (WSI) and tissue microarray images (TMA) which are providing new insights into the causes of cancer. The challenge is to extract and process effectively all the information in order to characterize all the heterogeneous tissue-derived data. This study aims to identify an optimal set of features that best separates different classes in breast TMA. These classes are: stroma, adipose tissue, benign and benign anomalous structures and ductal and lobular carcinomas. To this end, we propose an exhaustive assessment on the utility of textons and colour for automatic classification of breast TMA. Frequential and spatial texton maps from eight different colour models were extracted and compared. Then, in a novel way, the TMA is characterized by the 1st and 2nd order Haralick statistical descriptors obtained from the texton maps with a total of 241 × 8 features for each original RGB image. Subsequently, a feature selection process is performed to remove redundant information and therefore to reduce the dimensionality of the feature vector. Three methods were evaluated: linear discriminant analysis, correlation and sequential forward search. Finally, an extended bank of classifiers composed of six techniques was compared, but only three of them could significantly improve accuracy rates: Fisher, Bagging Trees and AdaBoost. Our results reveal that the combination of different colour models applied to spatial texton maps provides the most efficient representation of the breast TMA. Specifically, we found that the best colour model combination is Hb, Luv and SCT for all classifiers and the classifier that performs best for all colour model combinations is the AdaBoost. On a database comprising 628 TMA images, classification yields an accuracy of 98.1% and a precision of 96.2% with a total of 316 features on spatial textons maps.
authors.affiliation: VISILAB, E.T.S. Ingenieros Industriales, Universidad de Castilla-La Mancha, Ciudad Real, Spain. Electronic address: MMilagro.fernandez@uclm.es.
authors.forename: M Milagro
authors.initials: MM
authors.lastname: Fernández-Carrobles
authors.affiliation: VISILAB, E.T.S. Ingenieros Industriales, Universidad de Castilla-La Mancha, Ciudad Real, Spain. Electronic address: Gloria.Bueno@uclm.es.
authors.forename: Gloria
authors.initials: G
authors.lastname: Bueno
authors.affiliation: VISILAB, E.T.S. Ingenieros Industriales, Universidad de Castilla-La Mancha, Ciudad Real, Spain.
authors.forename: Oscar
authors.initials: O
authors.lastname: Déniz
authors.affiliation: VISILAB, E.T.S. Ingenieros Industriales, Universidad de Castilla-La Mancha, Ciudad Real, Spain.
authors.forename: Jesús
authors.initials: J
authors.lastname: Salido
authors.affiliation: Department of Pathology, Hospital de Jerez de la Frontera, Spain.
authors.forename: Marcial
authors.initials: M
authors.lastname: García-Rojo
authors.affiliation: Department of Pathology, Hospital General Universitario de Ciudad Real, Spain.
authors.forename: Lucía
authors.initials: L
authors.lastname: Gonzández-López
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification* / Q000473:pathology*; D003116:Color; D003124:Colorimetry / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D000069550:Machine Learning; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D009626:Terminology as Topic; D046888:Tissue Array Analysis / Q000379:methods*
keywords: Automatic classification; Colour models; Digital pathology; Feature selection; Image texture analysis; TMA (tissue microarray); Texton maps