On the classification of simple and complex biological images using Krawtchouk moments and Generalized pseudo-Zernike moments: a case study with fly wing images and breast cancer mammograms

Abstract

In image analysis, orthogonal moments are useful mathematical transformations for creating new features from digital images. Moreover, orthogonal moment invariants produce image features that are resistant to translation, rotation, and scaling operations. Here, we show the result of a case study in biological image analysis to help researchers judge the potential efficacy of image features derived from orthogonal moments in a machine learning context. In taxonomic classification of forensically important flies from the Sarcophagidae and the Calliphoridae family (n = 74), we found the GUIDE random forests model was able to completely classify samples from 15 different species correctly based on Krawtchouk moment invariant features generated from fly wing images, with zero out-of-bag error probability. For the more challenging problem of classifying breast masses based solely on digital mammograms from the CBIS-DDSM database (n = 1,151), we found that image features generated from the Generalized pseudo-Zernike moments and the Krawtchouk moments only enabled the GUIDE kernel model to achieve modest classification performance. However, using the predicted probability of malignancy from GUIDE as a feature together with five expert features resulted in a reasonably good model that has mean sensitivity of 85%, mean specificity of 61%, and mean accuracy of 70%. We conclude that orthogonal moments have high potential as informative image features in taxonomic classification problems where the patterns of biological variations are not overly complex. For more complicated and heterogeneous patterns of biological variations such as those present in medical images, relying on orthogonal moments alone to reach strong classification performance is unrealistic, but integrating prediction result using them with carefully selected expert features may still produce reasonably good prediction models.

Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species

“…Our present results consolidate the findings in Goh and Khang (2021) that Krawtchouk moment invariants features extracted from whole‐image analysis of fly wing venation patterns enable a random forests classifier to produce highly accurate species identifications. Importantly, our findings have established a high accuracy lower limit (~91%) that is attainable in identification of fly species based on their wing venation patterns.…”

“…bezziana , Chrysomya megacephala (Fabricius, 1794) (Diptera: Calliphoridae) and Chrysomya rufifacies (Macquart, 1843) (Diptera: Calliphoridae) at the species level. More recently, Goh and Khang (2021) applied a class of mathematical transformation known as Krawtchouk moment invariants onto pixel data from binarised fly wing images. They found that random forests classification of 12 fly species produced an out‐of‐bag misclassification rate of 0%, compared to about 39% using geometric morphometric data.…”

“…In veterinary and medical entomology, Macleod et al [19] first demonstrated the applicability of pixel-based image analysis for identification of Chrysomya bezziana at the sex and population-level, as well as identification of Chrysomya bezziana, Chrysomya megacephala, and Chrysomya rufifacies at the species-level. More recently, Goh & Khang [20] applied a class of mathematical transformation known as Krawtchouk moment invariants onto pixel data from binarised fly wing images. They found that random forests classification of 12 fly species produced an out-of-bag misclassification rate of 0%, compared to about 39% using geometric morphometric data.…”

“…However, the misclassification rates were likely underestimated because small sample sizes (n=74) prevented meaningful training and test samples from being constructed. In this paper, we aim to provide a definitive validation of the usefulness of the approach in Goh & Khang [20] using a substantially larger set of samples with proper test sample estimation of misclassification rate.…”

Accurate Identification of Fly Species in Forensic, Medical and Veterinary Entomology using Wing Venation Patterns

“…Our present results consolidate the findings in Goh & Khang [20], that Krawtchouk moment invariants features extracted from whole-image analysis of fly wing venation patterns enable a random forests classifier to produce highly accurate species identifications. Importantly, our findings have set a high lower bound for the degree of accuracy (~91%) that is attainable in identification of fly species based on their wing venation patterns.…”