scholarly journals 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

2021 ◽  
Vol 7 ◽  
pp. e698
Author(s):  
Jia Yin Goh ◽  
Tsung Fei Khang
Keyword(s):  
Image Analysis ◽  
Classification Performance ◽  
Image Features ◽  
Zernike Moments ◽  
Taxonomic Classification ◽  
Learning Context ◽  
Orthogonal Moments ◽  
Krawtchouk Moments

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.

scholarly journals Object-based analysis of UAS imagery to map emergent and submerged invasive aquatic vegetation: a case study

2017 ◽  
Vol 5 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Dominique Chabot ◽  
Christopher Dillon ◽  
Oumer Ahmed ◽  
Adam Shemrock
Keyword(s):  
Image Analysis ◽  
Data Collection ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Weather Conditions ◽  
Classification Performance ◽  
Unmanned Aircraft ◽  
Automated Image Analysis ◽  
Object Based

Small unmanned aircraft systems (UAS) combined with automated image analysis may provide an efficient alternative or complement to labour-intensive boat-based monitoring of invasive aquatic vegetation. A small mapping drone was assessed for collecting high-resolution (≤5 cm/pixel) true-colour and near-infrared imagery revealing the distribution of invasive water soldier (Stratiotes aloides) in the Trent–Severn Waterway, Ontario (Canada). We further evaluated the capacity of an object-based image analysis approach based on the Random Forests classification algorithm to map features in the imagery, chiefly emergent and submerged water soldier colonies. The imagery contained flaws and inconsistencies resulting from data collection in suboptimal weather conditions that likely negatively impacted classification performance. Nevertheless, our best-performing classification had a producer’s and user’s accuracy for water soldier of 81% and 74%, respectively, an overall accuracy of 78%, and a kappa value of 61%, indicating “substantial” accuracy. This trial provides an instructive case study on results achieved in a “real-world” application of a UAS for environmental monitoring, notably characterized by time constraints for data collection and analysis. Beyond avoiding data collection in unfavourable weather conditions, adaptations of the image segmentation process and use of a true discrete-band multispectral camera may help to improve classification accuracy, particularly of submerged vegetation.

Scene Classification Based on Improved Spatial Partition Model

2014 ◽  
Vol 527 ◽  
pp. 339-342
Author(s):  
Zhi Yuan Liu ◽  
Jin He ◽  
Jin Long Wang ◽  
Fei Zhao
Keyword(s):  
Spatial Information ◽  
Classification Performance ◽  
Image Features ◽  
Natural Scene ◽  
Scene Classification ◽  
Partition Model ◽  
Spatial Partition ◽  
Improved Model ◽  
Local Image

In order to make full use of the spatial information of images in the classification of natural scene, we use the spatial partition model. But mechanically space division caused the abuse of spatial information. So spatial partition model must be properly improved to make the different categories of images were more diversity, so that the classification performance is improved. In addition, to further improve the performance, we use FAN-SIFT as local image features. Experiments made on 8 scenes image dataset and Caltech101 dataset show that the improved model can obtain better classification performance.

scholarly journals Orthogonal Moment Extraction and Classification of Melanoma Images

2018 ◽  
Author(s):  
Sudhakar Singh ◽  
Shabana Urooj
Keyword(s):  
Support Vector Machine ◽  
Zernike Moments ◽  
Support Vector ◽  
Moment Invariants ◽  
Swarm Optimization ◽  
Orthogonal Moments ◽  
Equal Interval ◽  
The Moment ◽  
Phase Angles

This paper provides orthogonal moments (OM) such as, Zernike Moments(ZM), Psuedo Zernike Moments(PZM) and Orthogonal Fourier Mellin Moments(OFMM) for the analysis of melanoma images. The moment invariants may vary with respect to geometric variations. For the analysis of orthogonal moments hundred random melanoma images and hundred non-melanoma images have been taken into consideration from the database of 570 melanoma images and 250 non-melanoma images respectively. Orthoganal moments have been computed by varying the phase angles from 10° to 40° with an equal interval of 10° degree for the orders 2, 4,8,16,32,64,128,256 respectively. For the optimal OMs Particle Swarm Optimization (PSO) technique have been used. These set of extracted optimal OMs have been further applied to classify melanoma images. Support Vector Machine (SVM) has been used for the classification of [1]sensitivity=88.78%.

scholarly journals Image analysis of real-time classification of cherry fruit from colour features

2021 ◽  
Vol 52 (4) ◽  
Author(s):  
José F. Reyes ◽  
Elías Contreras ◽  
Christian Correa ◽  
Pedro Melin
Keyword(s):  
Image Analysis ◽  
Real Time ◽  
Low Cost ◽  
Image Features ◽  
Colour Pattern ◽  
Image Analysis Algorithm ◽  
Colour Patterns ◽  
Sample Set ◽  
Colour Classes

An image analysis algorithm for the classification of cherries in real time by processing their digitalized colour images was developed, and tested. A set of five digitalized images of colour pattern, corresponding to five colour classes defined for commercial cherries, was characterized. The algorithm performs the segmentation of the cheery image by rejecting the pixels of the background and keeping the image features corresponding to the coloured area of the fruit. A histogram analysis was carried out for the RGB and HSV colour spaces, where the Red and Hue components showed differences between each of the specified colour patterns of the exporting reference system. This information led to the development of a hybrid Bayesian classification algorithm based on the components R and H. Its accuracy was tested with a set of cherry samples within the colour range of interest. The algorithm was implemented by means of a real time C++ code in Microsoft Visual Studio environment. When testing, the algorithm showed a 100% effectiveness in classifying a sample set of cherries into the five standardized cherry classes. The components of the hardware-software system for implementing the methodology are low cost, thus ensuring an affordable commercial deployment.

scholarly journals Classification of Dental Radiographs Using Deep Learning

2021 ◽  
Vol 10 (7) ◽  
pp. 1496
Author(s):  
Jose E. Cejudo ◽  
Akhilanand Chaurasia ◽  
Ben Feldberg ◽  
Joachim Krois ◽  
Falk Schwendicke
Keyword(s):  
Deep Learning ◽  
Classification Performance ◽  
Image Features ◽  
Dental Radiographs ◽  
Maxilla And Mandible ◽  
Expert Reasoning ◽  
Radiographic Data ◽  
Learning Architectures ◽  
Activation Mapping

Objectives: To retrospectively assess radiographic data and to prospectively classify radiographs (namely, panoramic, bitewing, periapical, and cephalometric images), we compared three deep learning architectures for their classification performance. Methods: Our dataset consisted of 31,288 panoramic, 43,598 periapical, 14,326 bitewing, and 1176 cephalometric radiographs from two centers (Berlin/Germany; Lucknow/India). For a subset of images L (32,381 images), image classifications were available and manually validated by an expert. The remaining subset of images U was iteratively annotated using active learning, with ResNet-34 being trained on L, least confidence informative sampling being performed on U, and the most uncertain image classifications from U being reviewed by a human expert and iteratively used for re-training. We then employed a baseline convolutional neural networks (CNN), a residual network (another ResNet-34, pretrained on ImageNet), and a capsule network (CapsNet) for classification. Early stopping was used to prevent overfitting. Evaluation of the model performances followed stratified k-fold cross-validation. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to provide visualizations of the weighted activations maps. Results: All three models showed high accuracy (>98%) with significantly higher accuracy, F1-score, precision, and sensitivity of ResNet than baseline CNN and CapsNet (p < 0.05). Specificity was not significantly different. ResNet achieved the best performance at small variance and fastest convergence. Misclassification was most common between bitewings and periapicals. For bitewings, model activation was most notable in the inter-arch space for periapicals interdentally, for panoramics on bony structures of maxilla and mandible, and for cephalometrics on the viscerocranium. Conclusions: Regardless of the models, high classification accuracies were achieved. Image features considered for classification were consistent with expert reasoning.

Image analysis-based classification of pigmentation patterns in fish: A case study of pseudo-albinism in Senegalese sole

Aquaculture ◽  
2016 ◽  
Vol 464 ◽  
pp. 303-308 ◽  
Author(s):  
Asher Wishkerman ◽  
Anaïs Boglino ◽  
Maria Jose Darias ◽  
Karl B. Andree ◽  
Alicia Estévez ◽  
...  
Keyword(s):  
Image Analysis ◽  
Senegalese Sole

scholarly journals On the Influence of Reference Mahalanobis Distance Space for Quality Classification of Complex Metal Parts Using Vibrations

2020 ◽  
Vol 10 (23) ◽  
pp. 8620
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Mahalanobis Distance ◽  
Turbine Blades ◽  
Classification Performance ◽  
Structural Quality ◽  
Quality Classification ◽  
Metallic Parts ◽  
Numerical Case Study ◽  
Damage Types

Mahalanobis distance (MD) is a well-known metric in multivariate analysis to separate groups or populations. In the context of the Mahalanobis-Taguchi system (MTS), a set of normal observations are used to obtain their MD values and construct a reference Mahalanobis distance space, for which a suitable classification threshold can then be introduced to classify new observations as normal/abnormal. Aiming at enhancing the performance of feature screening and threshold determination in MTS, the authors have recently proposed an integrated Mahalanobis classification system (IMCS) algorithm with robust classification performance. However, the reference MD space considered in either MTS or IMCS is only based on normal samples. In this paper, an investigation on the influence of the reference MD space based on a set of (i) normal samples, (ii) abnormal samples, and (iii) both normal and abnormal samples for classification is performed. The potential of using an alternative MD space is evaluated for sorting complex metallic parts, i.e., good/bad structural quality, based on their broadband vibrational spectra. Results are discussed for a sparse and imbalanced experimental case study of complex-shaped metallic turbine blades with various damage types; a rich and balanced numerical case study of dogbone-cylinders is also considered.

scholarly journals Effect of Quantitative Nuclear Image Features on Recurrence of Ductal Carcinoma In Situ (DCIS) of the Breast

2008 ◽  
Vol 6 ◽  
pp. CIN.S401 ◽  
Author(s):  
David E. Axelrod ◽  
Naomi A. Miller ◽  
H. Lavina Lickley ◽  
Jin Qian ◽  
William A. Christens-Barry ◽  
...  
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Image Features ◽  
Nuclear Grade ◽  
Image Feature ◽  
Group A ◽  
Group B ◽  
Nuclear Features

Background Nuclear grade has been associated with breast DCIS recurrence and progression to invasive carcinoma; however, our previous study of a cohort of patients with breast DCIS did not find such an association with outcome. Fifty percent of patients had heterogeneous DCIS with more than one nuclear grade. The aim of the current study was to investigate the effect of quantitative nuclear features assessed with digital image analysis on ipsilateral DCIS recurrence. Methods Hematoxylin and eosin stained slides for a cohort of 80 patients with primary breast DCIS were reviewed and two fields with representative grade (or grades) were identified by a Pathologist and simultaneously used for acquisition of digital images for each field. Van Nuys worst nuclear grade was assigned, as was predominant grade, and heterogeneous grading when present. Patients were grouped by heterogeneity of their nuclear grade: Group A: nuclear grade 1 only, nuclear grades 1 and 2, or nuclear grade 2 only (32 patients), Group B: nuclear grades 1, 2 and 3, or nuclear grades 2 and 3 (31 patients), Group 3: nuclear grade 3 only (17 patients). Nuclear fine structure was assessed by software which captured thirty-nine nuclear feature values describing nuclear morphometry, densitometry, and texture. Step-wise forward Cox regressions were performed with previous clinical and pathologic factors, and the new image analysis features. Results Duplicate measurements were similar for 89.7% to 97.4% of assessed image features. The rate of correct classification of nuclear grading with digital image analysis features was similar in the two fields, and pooled assessment across both fields. In the pooled assessment, a discriminant function with one nuclear morphometric and one texture feature was significantly (p = 0.001) associated with nuclear grading, and provided correct jackknifed classification of a patient's nuclear grade for Group A (78.1%), Group B (48.4%), and Group C (70.6%). The factors significantly associated with DCIS recurrence were those previously found, type of initial presentation (p = 0.03) and amount of parenchymal involvement (p = 0.05), along with the morphometry image feature of ellipticity (p = 0.04). Conclusion Analysis of nuclear features measured by image cytometry may contribute to the classification and prognosis of breast DCIS patients with more than one nuclear grade.

Sign in / Sign up

Export Citation Format

Share Document