scholarly journals A Novel Curvature - Norm Based Centroid Initialized and Morphologically Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

2018 ◽  
Vol 11 (3) ◽  
pp. 1335-1343
Author(s):  
P.M. Shivamurthy ◽  
T.N. Nagabhushan ◽  
Vijaya Basavaraj
Keyword(s):  
Level Sets ◽  
Active Contours ◽  
Geometric Approach ◽  
Cancer Tissue ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Novel Approach ◽  
Accuracy Measure ◽  
Malignant Breast ◽  
Histopathological Images

Nuclear pleomorphism is considered to be one of the most significant shape based feature adapted in grading the cancer through the pathological studies of the H&E stained tissue slides. Microscopic study of manually extracting the feature is highly laborious and misleads the pathologists during grading. Digitization of the slides has given rise to various segmentation approaches to extract the nuclei shape to assess the degree of pleomorphism. Here, a novel approach of initializing and evolving the distance regularized level sets (DRLS) for the detection and segmentation of the nuclei has been presented. In this work, two major objectives have been achieved. First, a novel geometric approach has been devised for the detection of centroids of each nuclei in the occluded region and second, a shape prior model has been presented for the extraction of gradient information through morphological operations. The multiple level set implementation of the DRLS contours are initialized using the centroids detected and driven through the gradient computed. The proposed method has been experimented over the images of benign and malignant breast cancer tissue obtained from BeakHis dataset. A quantitative analysis of the results have shown that a 97% of object detection accuracy and 78% of overlap resolution has been achieved through the proposed model. A comparative study with that of geodesic active contours have indicated an improvement in the segmentation accuracy measure of 9-10 pixel difference.

Spherical object recognition based on the number of contour edges extracted by fitting and convex hull processing

2020 ◽  
pp. 095440702096950
Author(s):  
Bochang Zou ◽  
Huadong Qiu ◽  
Yufeng Lu
Keyword(s):  
Convex Hull ◽  
Target Object ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Circle Detection ◽  
Spherical Object ◽  
Single Feature ◽  
Contour Fitting ◽  
Rgb Images

The detection of spherical targets in workpiece shape clustering and fruit classification tasks is challenging. Spherical targets produce low detection accuracy in complex fields, and single-feature processing cannot accurately recognize spheres. Therefore, a novel spherical descriptor (SD) for contour fitting and convex hull processing is proposed. The SD achieves image de-noising by combining flooding processing and morphological operations. The number of polygon-fitted edges is obtained by convex hull processing based on contour extraction and fitting, and two RGB images of the same group of objects are obtained from different directions. The two fitted edges of the same target object obtained at two RGB images are extracted to form a two-dimensional array. The target object is defined as a sphere if the two values of the array are greater than a custom threshold. The first classification result is obtained by an improved K-NN algorithm. Circle detection is then performed on the results using improved Hough circle detection. We abbreviate it as a new Hough transform sphere descriptor (HSD). Experiments demonstrate that recognition of spherical objects is obtained with 98.8% accuracy. Therefore, experimental results show that our method is compared with other latest methods, HSD has higher identification accuracy than other methods.

scholarly journals A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI

2021 ◽  
Vol 11 (2) ◽  
pp. 674
Author(s):  
Marianna Koctúrová ◽  
Jozef Juhár
Keyword(s):  
Visual Stimuli ◽  
Computer Interface ◽  
Single Subject ◽  
Detection Accuracy ◽  
The Novel ◽  
Activity Detection ◽  
Speech Activity ◽  
Novel Approach ◽  
Eeg Data ◽  
Speech Activity Detection

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

scholarly journals Novel metric for hyperbolic phylogenetic tree embeddings

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Hirotaka Matsumoto ◽  
Takahiro Mimori ◽  
Tsukasa Fukunaga
Keyword(s):  
Phylogenetic Tree ◽  
Hyperbolic Space ◽  
Nearest Neighbor ◽  
Cancer Genomics ◽  
Geometric Approach ◽  
Artificial Intelligence Research ◽  
Phylogenetic Methods ◽  
Novel Approach ◽  
Multiple Trees ◽  
Euclidean Embeddings

Abstract Advances in experimental technologies, such as DNA sequencing, have opened up new avenues for the applications of phylogenetic methods to various fields beyond their traditional application in evolutionary investigations, extending to the fields of development, differentiation, cancer genomics, and immunogenomics. Thus, the importance of phylogenetic methods is increasingly being recognized, and the development of a novel phylogenetic approach can contribute to several areas of research. Recently, the use of hyperbolic geometry has attracted attention in artificial intelligence research. Hyperbolic space can better represent a hierarchical structure compared to Euclidean space, and can therefore be useful for describing and analyzing a phylogenetic tree. In this study, we developed a novel metric that considers the characteristics of a phylogenetic tree for representation in hyperbolic space. We compared the performance of the proposed hyperbolic embeddings, general hyperbolic embeddings, and Euclidean embeddings, and confirmed that our method could be used to more precisely reconstruct evolutionary distance. We also demonstrate that our approach is useful for predicting the nearest-neighbor node in a partial phylogenetic tree with missing nodes. Furthermore, we proposed a novel approach based on our metric to integrate multiple trees for analyzing tree nodes or imputing missing distances. This study highlights the utility of adopting a geometric approach for further advancing the applications of phylogenetic methods.

Multimodal Retinal Image Registration for Optic Disk Segmentation

2004 ◽  
Vol 43 (04) ◽  
pp. 336-342 ◽  
Author(s):  
M. Skokan ◽  
L. Kubečka ◽  
M. Wolf ◽  
K. Donath ◽  
J. Jan ◽  
...  
Keyword(s):  
Nonlinear Filtering ◽  
Active Contours ◽  
Optic Disk ◽  
Scanning Laser ◽  
Morphological Operations ◽  
Anatomical Structures ◽  
Glaucoma Diagnosis ◽  
Laser Tomography ◽  
Canny Edge ◽  
Fundus Photographs

Summary Objectives: The analysis of the optic disk morphology with the means of the scanning laser tomography is an important step for glaucoma diagnosis. A method we developed for optic disk segmentation in images of the scanning laser tomograph is limited by noise, nonuniform illumination and presence of blood vessels. Inspired by recent medical research, we wanted to develop a tool for improving optic disk segmentation by registration of images of the scanning laser tomograph and color fundus photographs and by applying a method we developed for optic disk segmentation in color fundus photographs. Methods: The segmentation of the optic disk for glaucoma diagnosis in images of the scanning laser tomograph is based on morphological operations, detection of anatomical structures and active contours and has been described in a previous paper [1]. The segmentation of the optic disk in the fundus photographs is based on nonlinear filtering, Canny edge detector and a modified Hough transform. The registration is based on mutual information using simulated annealing for finding maxima. Results: The registration was successful 86.8% of the time when tested on 174 images. Results of the registration have shown a very low displacement error of a maximum of about 5 pixels. The correctness of the registration was manually evaluated by measuring distances between the real vessel borders and those from the registered image. Conclusions: We have developed a method for the registration of images of the scanning laser tomograph and fundus photographs. Our first experiments showed that the optic disk segmentation could be improved by fused information from both image modalities.

Foreign Circular Element Detection in Chest X-Rays for Effective Automated Pulmonary Abnormality Screening

2017 ◽  
Vol 7 (2) ◽  
pp. 36-49 ◽  
Author(s):  
Fatema Tuz Zohora ◽  
K.C. Santosh
Keyword(s):  
Computational Time ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Potential Candidate ◽  
X Rays ◽  
Detection Algorithms ◽  
Digital Chest ◽  
Chest X Ray ◽  
Pulmonary Abnormality ◽  
A New Technique

In automated chest X-ray screening (to detect pulmonary abnormality: Tuberculosis (TB), for instance), the presence of foreign element such as buttons and medical devices hinders its performance. In this paper, using digital chest radiographs, the authors present a new technique to detect circular foreign element, within the lung regions. They first compute edge map by using several different edge detection algorithms, which is followed by morphological operations for potential candidate selection. These candidates are then confirmed by using circular Hough transform (CHT). In their test, the authors have achieved precision, recall, and F1 score of 96%, 90%, and 92%, respectively with lung segmentation. Compared to state-of-the-art work, their technique excels performance in terms of both detection accuracy and computational time.

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