Multifeature Quantification of Nuclear Properties from Images of H&E-Stained Biopsy Material for Investigating Changes in Nuclear Structure with Advancing CIN Grade

Abstract

Background Cervical dysplasia is a precancerous condition, and if left untreated, it may lead to cervical cancer, which is the second most common cancer in women. The purpose of this study was to investigate differences in nuclear properties of the H&E-stained biopsy material between low CIN and high CIN cases and associate those properties with the CIN grade. Methods The clinical material comprised hematoxylin and eosin- (H&E-) stained biopsy specimens from lesions of 44 patients diagnosed with cervical intraepithelial neoplasia (CIN). Four or five nonoverlapping microscopy images were digitized from each patient's H&E specimens, from regions indicated by the expert physician. Sixty-three textural and morphological nuclear features were generated for each patient's images. The Wilcoxon statistical test and the point biserial correlation were used to estimate each feature's discriminatory power between low CIN and high CIN cases and its correlation with the advancing CIN grade, respectively. Results Statistical analysis showed 19 features that quantify nuclear shape, size, and texture and sustain statistically significant differences between low CIN and high CIN cases. These findings revealed that nuclei in high CIN cases, as compared to nuclei in low CIN cases, have more irregular shape, are larger in size, are coarser in texture, contain higher edges, have higher local contrast, are more inhomogeneous, and comprise structures of different intensities. Conclusion A systematic statistical analysis of nucleus features, quantified from the H&E-stained biopsy material, showed that there are significant differences in the shape, size, and texture of nuclei between low CIN and high CIN cases.

Quality Assessment of Pork and Turkey Hams Using FT-IR Spectroscopy, Colorimetric, and Image Analysis

“…Feature a* is a color feature and it quantifies image redness/greenness. GLNU is a second-order statistic [ 13 , 18 ] and it quantifies unevenness in the distribution of image structures throughout the grey levels. This three-feature combination was used in the design of a high-performance pattern-recognition system that correctly classified 32 out of 34 boiled turkey slices (two slices were wrongly assigned to the smoked-turkey class), and 31 out of 32 smoked turkey slices (one slice was wrongly classified to the boiled-turkey class).…”

“…Four features were calculated from the grayscale-image histogram (first-order statistics: mean value, standard deviation, skewness, and kurtosis). Thirteen features were computed from the image’s co-occurrence matrix [ 13 ] (second-order statistics: angular second moment, contrast, correlation, sum of squares, inverse difference moment, entropy, sum entropy, sum average, sum variance, difference variance, difference entropy, information measure correlation I, information measure correlation II). Five features were computed from the image’s run-length matrix [ 13 ] (second-order statistics: short-run emphasis, long-run emphasis, gray-level nonuniformity, run-length nonuniformity, run percentage) [ 13 ].…”