scholarly journals Segmentation of Tumour Region on Breast Histopathology Images for Assessment of Glandular Formation in Breast Cancer Grading

2021 ◽  
Vol 2071 (1) ◽  
pp. 012051
Author(s):  
P A S Nor Rahim ◽  
N Mustafa ◽  
H Yazid ◽  
T Xiao Jian ◽  
S Daud ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Median Filter ◽  
Automatic Segmentation ◽  
Post Processing ◽  
Processing Stage ◽  
Tubule Formation ◽  
Segmentation Accuracy ◽  
Three Stages ◽  
Histopathology Images ◽  
Cancer Grading

Abstract Breast cancer is the most silent killer among cancers nowadays. NHG system is widely accepted worldwide as a gold standard in providing the overall grade to breast cancer. One of the breast cancer features used in the NHG system is tubule formation. Assessment of tubule formation requires pathologist to identify tumour regions. However, colour variation on breast histopathology could influence tumour regions detection on breast histopathology images. Manual identification of tumour regions using microscope may also vary between pathologists. Thus, automatic segmentation is crucial to segment tumour regions. In this study, a simple approach of segmentation was proposed to segment tumour region on breast histopathology images. The proposed segmentation involved three stages: pre-processing, segmentation and post-processing. The proposed approach using GHE and median filter in the pre-processing stage; Otsu thresholding in the segmentation stage and; morphological operation and pixel removal in the post-processing stage was found able to segment the tumour region with average segmentation accuracy of 90.4 %.

Effect of stage-dependent addition of nanoparticles in additive manufacturing

2018 ◽  
Vol 33 (3) ◽  
pp. 357-376 ◽  
Author(s):  
Vishal Francis ◽  
Prashant K Jain
Keyword(s):  
Additive Manufacturing ◽  
Material Properties ◽  
Application Domain ◽  
Post Processing ◽  
Processing Stage ◽  
Structure Morphology ◽  
Three Stages ◽  
End Use ◽  
Limited Choice

Recent advancements in the additive manufacturing (AM) technology have increased its utilization in various engineering sectors for the development of end-use products. However, the limited choice of available materials tends to limit its application domain. Addition of nanoparticles can significantly improve the material properties of the AM parts. Moreover, nanoparticles can be added in different stages of the process which will play an important role in determining the increase in material properties. This aspect of the stage-dependent addition of nanoparticles in AM process has not been fully explored. The present work discusses the effect of adding nanoclay in three stages of AM process namely preprocessing, on-site and post-processing stage. It has been found that the nanoparticles interact in a different way with the polymer and result in different structure, morphology and mesostructure of the nanocomposites. The approach can be utilized for achieving improved material properties of AM-fabricated parts.

scholarly journals BREAST CANCER GRADING OF H&E STAINED HISTOPATHOLOGY IMAGES

2018 ◽  
Vol 9 (1) ◽  
pp. 1827-1832
Author(s):  
Vijay M. Mane ◽  
Nikhil Tagalpallewar
Keyword(s):  
Breast Cancer ◽  
Histopathology Images ◽  
Cancer Grading

Combination of Gray-Level and Moment Invariant for Automatic Blood Vessel Detection on Retinal Image

2017 ◽  
Vol 34 ◽  
pp. 10-19 ◽  
Author(s):  
Wan Azani Mustafa ◽  
Haniza Yazid ◽  
Wahida Kamaruddin
Keyword(s):  
Feature Extraction ◽  
Blood Vessels ◽  
Automatic Segmentation ◽  
Objective Evaluation ◽  
Gray Level ◽  
Post Processing ◽  
Moment Invariant ◽  
Blood Vessel Detection ◽  
Vessel Enhancement ◽  
Three Stages

Segmentation of blood vessels in the retinal is a crucial step in the diagnosis of eye diseases such as diabetic retinopathy and glaucoma. This paper presents a supervised method for automatic segmentation of blood vessels in retinal images. The proposed method based on a hybrid combination between Gray-Level and Moment Invariant techniques. There are four steps involved, whereas preprocessing, feature extraction, classification, and post-processing. In the preprocessing, three stages are performed include vessel central light reflex removal, background homogenization, and vessel enhancement. The 7-D vector feature extraction was performed to compute that compose of gray-level and moment invariants-based features for pixel representation. The decision tree is used for classification step that characterized the pixel based on vessels and non-vessels. The final step is the post-processing which will remove the small artifacts appears after classification process. The proposed method was compared to the Vascular Tree method and Morphological method. Based on the objective evaluation, the proposed method achieved (sensitivity = 98.589, specificity = 55.544 and accuracy = 96.197).

scholarly journals Detection of Blast Cells in Microscopic Images Using Optimization Algorithms

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2443-2446
Keyword(s):  
Median Filter ◽  
Optimization Algorithms ◽  
Diagnostic Process ◽  
Detection Accuracy ◽  
Post Processing ◽  
Processing Methods ◽  
Blood Cancer ◽  
Microscopic Images ◽  
Standardized Report ◽  
Segmentation Accuracy

Now a day’s in medical field, the most prevalent and hazardous disease is blood cancer. It starts in bone marrow where the blood is produced and prevents many of its regular functions. At an approximation for every three minutes one person in the world is diagnosed with the blood cancer. Early detection of the cancer is necessary for the proper treatment, so as to save the lives of mankind. In general the blood cancer diagnosis will be performed by visual examination of the blood samples of the patient under microscope. The blood cancer detection accuracy of this method depends on the technical skilling abilities of the operator and often leads non-standardized report. To improve patient diagnosis various image processing methods are developed to extract useful information from microscopic images. This could help Hematologists in their diagnostic process. In this paper pre-processing and post-processing methods are applied on microscopic images. These images will be acquired from either public or private database. In this work the images were collected from Microscopyu which is a public database. Pre-processing methods involves color conversion i.e. RGB to grayscale, removal of noise by median filter and an improved contrast enhancement technique CLAHE is implemented. Later Post-processing methods are applied. In this stage Otsu segmentation and optimization algorithms are combined for improving segmentation accuracy. Optimization algorithms used are Particle Swarm optimization (PSO) and Cuckoo Search algorithms (CSO) and Finally features of the segmented image can be extracted from d Scale Invariant Feature Transform (SIFT) . In this work existing method is PSO and proposed method is CSO. At the end the qualitative analysis of the work is done through the statistical parameters like segmentation accuracy, sensitivity, specificity, PSNR and CPU time.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Automatic speech segmentation using throat-acoustic correlation coefficients

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Rustam Rafikovich Mussabayev ◽  
Maksat N. Kalimoldayev ◽  
Yedilkhan N. Amirgaliyev ◽  
Timur R. Mussabayev
Keyword(s):  
Large Volume ◽  
Automatic Segmentation ◽  
Correlation Coefficients ◽  
High Accuracy ◽  
Speech Segmentation ◽  
Preliminary Training ◽  
Effi Ciency ◽  
Segmentation Accuracy ◽  
New Type ◽  
The Given

Abstract This work considers one of the approaches to the solution of the task of discrete speech signal automatic segmentation. The aim of this work is to construct such an algorithm which should meet the following requirements: segmentation of a signal into acoustically homogeneous segments, high accuracy and segmentation speed, unambiguity and reproducibility of segmentation results, lack of necessity of preliminary training with the use of a special set consisting of manually segmented signals. Development of the algorithm which corresponds to the given requirements was conditioned by the necessity of formation of automatically segmented speech databases that have a large volume. One of the new approaches to the solution of this task is viewed in this article. For this purpose we use the new type of informative features named TAC-coefficients (Throat-Acoustic Correlation coefficients) which provide sufficient segmentation accuracy and effi- ciency.

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

2018 ◽  
Vol 30 (03) ◽  
pp. 1850024 ◽  
Author(s):  
Zeinab Heidari ◽  
Mehrdad Dadgostar ◽  
Zahra Einalou
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Image Data ◽  
Thermal Images ◽  
Early Stages ◽  
Invasive Method

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

Sign in / Sign up

Export Citation Format

Share Document