HEp-2 cell classification and segmentation using motif texture patterns and spatial features with random forests

Prasath, V. B. Surya; Kassim, Yasmin M.; Oraibi, Zakariya A.; Guiriec, Jean-Baptiste; Hafiane, Adel; Seetharaman, Guna; Palaniappan, Kannappan

doi:10.1109/icpr.2016.7899614

Cited by 23 publications

(20 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the ICPR-2014 dataset (same as the ICIP-2013 dataset) is the most recent and of much larger scale than that of ICPR12, we use this dataset for validation and compare our framework. This is similar to other works such as [11,[30][31][32][33][34] which also use the same dataset. More recent reviews of this topic were provided by [6,35].…”

Section: Related Worksupporting

confidence: 66%

“…Due to this reason, we are not adding table for protocol 2. Table 9 illustrates the performance comparison (using both the protocols) among various methods, where in [30][31][32] utilized random forest for classification, [11,33] are recent methods using, arguably, more sophisticated features, and the work of [34] uses deep learning. For the methods in [33,34], the numbers in brackets denote the results with data augmentation.…”

Section: Results: Protocolmentioning

confidence: 99%

“…Random forest has only been utilized as classifier for this problem. Prasath et al [30] utilized texture features such as rotational invariant co-occurrence (RIC) versions of the well-known local binary pattern (LBP), median binary pattern (MBP), joint adaptive median binary pattern (JAMBP), and motif co-occurrence matrix (MCM) along with other optimized features, and reported mean class accuracy using different classifiers such as the k-nearest neighbors (kNN), support vector machine (SVM), and random forest (RF). The highest accuracy is obtained with RIC-LBP combined with a RIC variant of MCM.…”

Section: Related Workmentioning

confidence: 99%

“…In the paper [7] which is associated with the dataset used in this work, mean class accuracy (MCA) was used to evaluate the performance. Here, we utilize mean class accuracy, and in addition we also employ overall accuracy and false positive as evaluation measures for fair comparison with other state-of-art methods [11,[30][31][32][33][34] which also use the same metrics.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…As such, many different staining patterns (approximately [30][31][32][33][34][35] are reported [3,4], on the basis of the formation of molecular complex at different sites. However, it is observed that only few of these staining patterns are clinically more significant and are often detected by indirect immunofuorescence (IIF) microscopy on HEp-2 cells in the sera of patients with autoimmune disease [5].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

Gupta

Bhavsar

2018

J. Imaging

View full text Add to dashboard Cite

Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS) methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods). Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest) is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

show abstract