Human Epithelial Type 2 Cell Classification Research Articles

HEp-2 CELL CLASSIFICATION BY ADAPTIVE CONVOLUTIONAL LAYER BASED CONVOLUTIONAL NEURAL NETWORK

Objective: The antinuclear antibodies (ANA) that present in the human serum have a link with various autoimmune diseases. Human Epithelial type-2 (HEp-2) cells acts as a substance in the Indirect Immuno fluorescence (IIF) test for diagnosing these autoimmune diseases. In recent times, the computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has drawn more interest. Though, they often pose limitations like large intra-class and small inter-class variations. Hence, various efforts have been performed to automate the procedure of HEp-2 cell classification. To overcome these problems, this research work intends to propose a new HEp-2 classification process. Materials and Methods: This is regulated by integrating two processes, namely, segmentation and classification. Initially, the segmentation of the HEp-2 cells is carried out by deploying the morphological operations. In this paper, two morphology operations are deployed called opening and closing. Further, the classification process is exploited by proposing a modified Convolutional Neural Network (CNN). The main objective is to classify the HEp-2 cells effectively (Centromere, Golgi, Homogeneous, Nucleolar, NuMem, and Speckled) and is made by exploiting the optimization concept. This is implanted by developing a new algorithm called Distance Sorting Lion Algorithm (DSLA), which selects the optimal convolutional layer in CNN. Results: Through the performance analysis, the performance of the proposed model for test case 1 at learning percentage 60 is 3.84%, 1.79%, 6.22%, 1.69%, and 5.53% better than PSO, FF, GWO, WOA, and LA, respectively. At 80, the performance of the proposed model is 5.77%, 6.46%, 3.95%, 3.24%, and 5.55% better from PSO, FF, GWO, WOA, and LA, respectively. Hence, the performance of the proposed work is proved over other models under different measures. Conclusion: Finally, the performance is evaluated by comparing it with the other conventional algorithms in terms of accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, MCC, F1-Score and FDR, and proves the efficacy of the proposed model.

Benchmarking HEp-2 specimen cells classification using linear discriminant analysis on higher order spectra features of cell shape

Human Epithelial Type-2 (HEp-2) specimen patterns in microscopic images have been widely used for diagnosing autoimmune diseases. Manual cell classification is inefficient, time consuming and subject to human error, therefore, researchers have attempted to automate the procedure of HEp-2 cell classification. Challenges include large data volumes, variation in cell densities, shape, rotation, scaling and shifting, and overlapped cells. A computer-aided diagnosis (CAD) system, based on image-based classification, can help in terms of effort, time, and reliability of classification HEp-2 specimens. HEp-2 cells are classified into seven classes: speckled, homogeneous, centromere, nuclear membrane, nucleolar, golgi, and mitotic spindle. In this work, we focus on the task of HEp-2 specimen cell classification in terms of feature extraction representations, features selection and classifier design. We propose a method of linear discriminant analysis relying on bispectral invariant features from a segmented HEp-2 specimen cell shape that allows us to generalize features to being variation in shape, rotation, scaling and shifting. HEp-2 specimen cell shape segmentation is determined using level set based forces in normal direction and geometric active contours. Both linear and kernel support vector machines as well as fuzzy C mean are employed for fast classification of specimens and achieved Mean Class Rates (MCAs) of 91.25%, 86.02% and 89.14%, respectively, over the ICPR2014 and ICPR2016 contests HEp-2 cell task-2 datasets. The results validate the competitive performance of the proposed system compared with other approaches reported on both contests.

Human Epithelial Type-2 Cell Image Classification Using an Artificial Neural Network with Hybrid Descriptors

ABSTRACTAntinuclear antibody (ANA) testing is best performed using the indirect immunofluorescence (IIF) method with human epithelial type-2 (HEp-2) cells as the substrate. IIF is a subjective procedure in which HEp-2 patterns are analyzed manually from the microscope. Therefore, ANA test results greatly rely on the experience and expertise of pathologists. Hence, complete automation of the ANA test is required to avoid incorrect diagnoses. This paper represents an algorithm for the complex HEp-2 cell classification problem. The proposed algorithm used a small hybrid feature set that characterizes the texture and morphology of the HEp-2 cells along with artificial neural network (ANN). The hybrid features were extracted by breaking up the image into eight binary images. The proposed hybrid descriptors were more efficient than the popular co-occurrence matrix descriptor and local binary pattern descriptors for texture analysis. The proposed algorithm was evaluated on the ICPR 2016 IIF HEp-2 cell image dataset. The results indicated that the hybrid descriptor with an ANN approach achieved improved performance, with “96.8%” mean class accuracy.

Deep cross residual network for HEp-2 cell staining pattern classification

Many computer-aided systems have been developed for Human epithelial type 2 (HEp-2) cell classification recently, but there is still a big performance gap between them and specialist doctors. Inspired by the recent successes of convolutional neural network, we proposed a deep cross residual network (DCRNet) for HEp-2 cell classification. A cross connection based residual block was proposed to increase the information flow among different network layers. We used two benchmark datasets to evaluate our system. The state-of-art results, i.e. the average class accuracy of 80.8% in the International Conference on Pattern Recognition (ICPR) 2012 dataset and the mean class accuracy of 85.1% in the Indirect Immunofluorescence Image (I3A) dataset, were achieved. Our result on the ICPR 2012 dataset is so far the best among all works reported in the literature. Our algorithm was winner of the most recent ICPR 2016 contest and the accuracy beat all of the top performers in the previous International Conference on Image Processing (ICIP) 2013 and the ICPR 2014 contests.

Accurate HEp-2 cell classification based on sparse bag of words coding

Autoimmune diseases (AD) are the abnormal response of the immune system of the body to healthy tissues. ADs have generally been on the increase. Efficient computer aided diagnosis of ADs through classification of the human epithelial type 2 (HEp-2) cells become beneficial. These methods make lower diagnosis costs, faster response and better diagnosis repeatability. In this paper, we present an automated HEp-2 cell image classification technique that exploits the sparse coding of the visual features together with the Bag of Words model (SBoW). In particular, SURF (Speeded Up Robust Features) and SIFT (Scale-invariant feature transform) features are specially integrated to work in a complementary fashion. This method helps greatly improve the cell classification accuracy. Additionally, a hierarchical max-pooling method is proposed to aggregate the local sparse codes in different layers to provide final feature vector. Furthermore, various parameters of the dictionary learning including the dictionary size, the learning iteration number, and the pooling strategy is also investigated. Experiments conducted on publicly available datasets show that the proposed technique clearly outperforms state-of-the-art techniques in cell and specimen levels.

Exploring illumination robust descriptors for human epithelial type 2 cell classification

Strong illumination variation is a key challenge in the Human Epithelial Type 2 (HEp-2) cell classification task. Aiming to improve the robustness of the HEp-2 classification system to the illumination variation, this paper deeply explores discriminative and illumination robust descriptors. Specifically, we propose a novel Spatial Shape Index Descriptor (SSID) to capture spatial layout information of the second-order structures, and utilize a Local Orientation Adaptive Descriptor (LOAD), which was originally designed for texture classification, to the HEp-2 cell classification task. Both SSID and LOAD show strong discrimination and great complementarity to each other.Four different sets of experiments were carried out to evaluate SSID, LOAD and their combination. Our two submissions achieved superior performance on the new Executable Thematic of Pattern Recognition Techniques for Indirect Immunofluorescence images analysis. Compared to the rank 1st method in the ICPR 2014 HEp-2 cell classification contest, both of our submissions achieved a better performance when only using the provided training data. Our approaches also demonstrated superior performance on a newly compiled large-scale HEp-2 data set with 63,445 cell images.

Accurate HEp-2 cell classification based on Sparse Coding of Superpixels

Autoimmune Diseases (AD) are among the top 10 leading causes of death in female children and women in all age groups up to 64 years. They are widely diagnosed by various antibody tests that typically apply the Indirect Immunofluorescence (IIF) to the Human Epithelial Type-2 (HEp-2) cells. Automated classification of Hep-2 cells has attracted much research interest in recent years, and many of these approaches employ patch-based models and the Bag of Words (BoW) scheme, but often face several typical constraints such as the need to process a huge number of overlapped image patches, tuning of various parameters and etc. We propose a superpixel based Hep-2 cell classification technique by calculating the sparse codes of image patches which are prepared in a more intelligent way. In particular, the superpixel approach guides the determination of the right image patches while aggregating the neighboring pixels of similar patterns. In addition, we introduce “extended superpixels” which is able to capture the most discriminative gradient information across the boundary of the HEp-2 cell images. The proposed technique has been evaluated over two public datasets (ICPR2012 and ICIP2013) and experiments show superior performance in both classification accuracy and speed of model training and cell classification.

HEp-2 cell classification: The role of Gaussian Scale Space Theory as a pre-processing approach

Indirect Immunofluorescence Imaging of Human Epithelial Type 2 (HEp-2) cells is an effective way to identify the presence of Anti-Nuclear Antibody (ANA). Most existing works on HEp-2 cell classification mainly focus on feature extraction, feature encoding and classifier design. Very few efforts have been devoted to study the importance of the pre-processing techniques. In this paper, we analyze the importance of the pre-processing, and investigate the role of Gaussian Scale Space (GSS) theory as a pre-processing approach for the HEp-2 cell classification task. We validate the GSS pre-processing under the Local Binary Pattern (LBP) and the Bag-of-Words (BoW) frameworks. Under the BoW framework, the introduced pre-processing approach, using only one Local Orientation Adaptive Descriptor (LOAD), achieved superior performance on the Executable Thematic on Pattern Recognition Techniques for Indirect Immunofluorescence (ET-PRT-IIF) image analysis. Our system, using only one feature, outperformed the winner of the ICPR 2014 contest that combined four types of features. Meanwhile, the proposed pre-processing method is not restricted to this work; it can be generalized to many existing works.

HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information.

Indirect immunofluorescence imaging of human epithelial type 2 (HEp-2) cell image is an effective evidence to diagnose autoimmune diseases. Recently, computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has attracted great attention. However, the HEp-2 cell classification task is quite challenging due to large intraclass and small interclass variations. In this paper, we propose an effective approach for the automatic HEp-2 cell classification by combining multiresolution co-occurrence texture and large regional shape information. To be more specific, we propose to: 1) capture multiresolution co-occurrence texture information by a novel pairwise rotation-invariant co-occurrence of local Gabor binary pattern descriptor; 2) depict large regional shape information by using an improved Fisher vector model with RootSIFT features, which are sampled from large image patches in multiple scales; and 3) combine both features. We evaluate systematically the proposed approach on the IEEE International Conference on Pattern Recognition (ICPR) 2012, the IEEE International Conference on Image Processing (ICIP) 2013, and the ICPR 2014 contest datasets. The proposed method based on the combination of the introduced two features outperforms the winners of the ICPR 2012 contest using the same experimental protocol. Our method also greatly improves the winner of the ICIP 2013 contest under four different experimental setups. Using the leave-one-specimen-out evaluation strategy, our method achieves comparable performance with the winner of the ICPR 2014 contest that combined four features.

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.

Automatic classification of Human Epithelial type 2 cell Indirect Immunofluorescence images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (e.g., speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is composed of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.