Computer-based classification of chromoendoscopy images using homogeneous texture descriptors

Object tracking in infrared images using a deep learning model and a target-attention mechanism

Small object tracking in infrared images is widely utilized in various fields, such as video surveillance, infrared guidance, and unmanned aerial vehicle monitoring. The existing small target detection strategies in infrared images suffer from submerging the target in heavy cluttered infrared (IR) maritime images. To overcome this issue, we use the original image and the corresponding encoded image to apply our model. We use the local directional number patterns algorithm to encode the original image to represent more unique details. Our model is able to learn more informative and unique features from the original and encoded image for visual tracking. In this study, we explore the best convolutional filters to obtain the best possible visual tracking results by finding those inactive to the backgrounds while active in the target region. To this end, the attention mechanism for the feature extracting framework is investigated comprising a scale-sensitive feature generation component and a discriminative feature generation module based on the gradients of regression and scoring losses. Comprehensive experiments have demonstrated that our pipeline obtains competitive results compared to recently published papers.

Elucidation of Differential Nano-Textural Attributes for Normal Oral Mucosa and Pre-Cancer

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.

Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images

Early diagnoses of esophageal cancer can greatly improve the survival rate of patients. At present, the lesion annotation of early esophageal cancers (EEC) in gastroscopic images is generally performed by medical personnel in a clinic. To reduce the effect of subjectivity and fatigue in manual annotation, computer-aided annotation is required. However, automated annotation of EEC lesions using images is a challenging task owing to the fine-grained variability in the appearance of EEC lesions. This study modifies the traditional EEC annotation framework and utilizes visual salient information to develop a two saliency levels-based lesion annotation (TSL-BLA) for EEC annotations on gastroscopic images. Unlike existing methods, the proposed framework has a strong ability of constraining false positive outputs. What is more, TSL-BLA is also placed an additional emphasis on the annotation of small EEC lesions. A total of 871 gastroscopic images from 231 patients were used to validate TSL-BLA. 365 of those images contain 434 EEC lesions and 506 images do not contain any lesions. 101 small lesion regions are extracted from the 434 lesions to further validate the performance of TSL-BLA. The experimental results show that the mean detection rate and Dice similarity coefficients of TSL-BLA were 97.24 and 75.15%, respectively. Compared with other state-of-the-art methods, TSL-BLA shows better performance. Moreover, it shows strong superiority when annotating small EEC lesions. It also produces fewer false positive outputs and has a fast running speed. Therefore, The proposed method has good application prospects in aiding clinical EEC diagnoses.

TriZ-a rotation-tolerant image feature and its application in endoscope-based disease diagnosis

Endoscopy is becoming one of the widely-used technologies to screen the gastric diseases, and it heavily relies on the experiences of the clinical endoscopists. The location, shape, and size are the typical patterns for the endoscopists to make the diagnosis decisions. The contrasting texture patterns also suggest the potential lesions. This study designed a novel rotation-tolerant image feature, TriZ, and demonstrated the effectiveness on both the rotation invariance and the lesion detection of three gastric lesion types, i.e., gastric polyp, gastric ulcer, and gastritis. TriZ achieved 87.0% in the four-class classification problem of the three gastric lesion types and the healthy controls, averaged over the twenty random runs of 10-fold cross-validations. Due to that biomedical imaging technologies may capture the lesion sites from different angles, the symmetric image feature extraction algorithm TriZ may facilitate the biomedical image based disease diagnosis modeling. Compared with the 378,434 features of the HOG algorithm, TriZ achieved a better accuracy using only 126 image features.

AUTOMATED ULCER AND BLEEDING CLASSIFICATION FROM WCE IMAGES USING MULTIPLE FEATURES FUSION AND SELECTION

In the area of medical imaging and computer vision, automatic diagnosis of ulcer and bleeding from wireless capsule endoscopy images has been an active research domain. It contains several challenges including low contrast, complex background, lesion shape and color which affect its segmentation and classification accuracy. In this article, a novel method for automated detection and classification of stomach infection is implemented. The proposed method consists of four major steps including preprocessing, lesion segmentation, image representation and classification. The lesion contrast is improved in preprocessing step by employing 3D-box filtering, 3D-median filtering and HSV transformation. In the second step, geometric features are extracted and applied to the saturated channel to give a binary image. The binary image is further improved by fusion of generated mask. After that, extraction of three types of features including color, shape and surf is performed from HSV and binary segmented images and their information is fused by a serial based method. A principal component analysis (PCA) and correlation coefficient based feature selection approach is proposed which are classified by multi class support vector machine (M-SVM). The proposed method is evaluated on personally collected images of three different classes including ulcer, bleeding and healthy. The M-SVM performs well with a maximum accuracy of 98.3% which shows the authenticity of presented method.

Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images

BACKGROUND AND OBJECTIVE

The early diagnosis of stomach cancer can be performed by using a proper screening procedure. Chromoendoscopy (CH) is an image-enhanced video endoscopy technique, which is used for inspection of the gastrointestinal-tract by spraying dyes to highlight the gastric mucosal structures. An endoscopy session can end up with generating a large number of video frames. Therefore, inspection of every individual endoscopic-frame is an exhaustive task for the medical experts. In contrast with manual inspection, the automated analysis of gastroenterology images using computer vision based techniques can provide assistance to endoscopist, by finding out abnormal frames from the whole endoscopic sequence.

METHODS

In this paper, we have presented a new feature extraction method named as Gabor-based gray-level co-occurrence matrix (G2LCM) for computer-aided detection of CH abnormal frames. It is a hybrid texture extraction approach which extracts a combination both local and global texture descriptors. Moreover, texture information of a CH image is represented by computing the gray level co-occurrence matrix of Gabor filters responses. Furthermore, the second-order statistics of these co-occurrence matrices are computed to represent images' texture.

RESULTS

The obtained results show the possibility to correctly classifying abnormal from normal frames, with sensitivity, specificity, accuracy, and area under the curve as 91%, 82%, 87% and 0.91 respectively, by using a support vector machine classifier and G2LCM texture features.

CONCLUSION

It is apparent from results that the proposed system can be used for providing aid to the gastroenterologist in the screening of the gastric tract. Ultimately, the time taken by an endoscopic procedure will be sufficiently reduced.