scholarly journals Impact of the accuracy of automatic segmentation of cell nuclei clusters on classification of thyroid follicular lesions

2014 ◽  
Vol 85 (8) ◽  
pp. 709-718 ◽  
Author(s):  
Chanho Jung ◽  
Changick Kim
Keyword(s):  
Automatic Segmentation ◽  
Cell Nuclei ◽  
Follicular Lesions

scholarly journals Automatic segmentation and classification of seven-segment display digits on auroral images

2016 ◽  
Vol 5 (2) ◽  
pp. 305-314 ◽  
Author(s):  
Tuomas Savolainen ◽  
Daniel Keith Whiter ◽  
Noora Partamies
Keyword(s):  
Automatic Segmentation ◽  
Scientific Information ◽  
Spherical Mirror ◽  
Automatic Method ◽  
Time Period ◽  
Film Images ◽  
Fully Automatic ◽  
Night Sky

Abstract. In this paper we describe a new and fully automatic method for segmenting and classifying digits in seven-segment displays. The method is applied to a dataset consisting of about 7 million auroral all-sky images taken during the time period of 1973–1997 at camera stations centred around Sodankylä observatory in northern Finland. In each image there is a clock display for the date and time together with the reflection of the whole night sky through a spherical mirror. The digitised film images of the night sky contain valuable scientific information but are impractical to use without an automatic method for extracting the date–time from the display. We describe the implementation and the results of such a method in detail in this paper.

Automatic segmentation and classification of breast masses from mammography using machine learning

2021 ◽  
Author(s):  
Prerna Chander ◽  
Lina Eilouti ◽  
Saubia Khan ◽  
Salam Dhou ◽  
Michel Pasquier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Segmentation ◽  
Breast Masses

scholarly journals Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

2020 ◽  
Vol 10 (14) ◽  
pp. 4791 ◽  
Author(s):  
Pedro Narváez ◽  
Steven Gutierrez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Automatic Classification ◽  
The State ◽  
Heart Sounds ◽  
Empirical Wavelet Transform ◽  
Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

Automatic segmentation and classification of gestational sac based on mean sac diameter using medical ultrasound image

2014 ◽  
Author(s):  
Shan Khazendar ◽  
Jessica Farren ◽  
Hisham Al-Assam ◽  
Ahmed Sayasneh ◽  
Hongbo Du ◽  
...  
Keyword(s):  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Medical Ultrasound ◽  
Gestational Sac

Automatic segmentation, counting, size determination and classification of white blood cells

Measurement ◽  
2014 ◽  
Vol 55 ◽  
pp. 58-65 ◽  
Author(s):  
Sedat Nazlibilek ◽  
Deniz Karacor ◽  
Tuncay Ercan ◽  
Murat Husnu Sazli ◽  
Osman Kalender ◽  
...  
Keyword(s):  
Blood Cells ◽  
Automatic Segmentation ◽  
White Blood Cells ◽  
Size Determination

scholarly journals Nonrigid Registration of 2-D and 3-D Dynamic Cell Nuclei Images for Improved Classification of Subcellular Particle Motion

2011 ◽  
Vol 20 (4) ◽  
pp. 1011-1022 ◽  
Author(s):  
Il-Han Kim ◽  
Yi-Chun M. Chen ◽  
David L. Spector ◽  
Roland Eils ◽  
Karl Rohr
Keyword(s):  
Particle Motion ◽  
Nonrigid Registration ◽  
Cell Nuclei ◽  
Dynamic Cell
Sign in / Sign up

Export Citation Format

Share Document