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

scholarly journals Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method

2014 ◽  
Vol 13 (1) ◽  
pp. 157 ◽  
Author(s):  
Rishu Gupta ◽  
Irraivan Elamvazuthi ◽  
Sarat Dass ◽  
Ibrahima Faye ◽  
Pandian Vasant ◽  
...  
Keyword(s):  
Diagnostic Method ◽  
Automatic Segmentation ◽  
Ultrasound Image ◽  
Supraspinatus Tendon

Medical Ultrasound Image Segmentation Based on Improved MultiResUNet Network

2021 ◽  
Author(s):  
Xinze Li ◽  
Wei Shi ◽  
Yang Jiao ◽  
Chen Yang ◽  
Ninghao Wang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Ultrasound Image ◽  
Medical Ultrasound ◽  
Ultrasound Image Segmentation

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.

scholarly journals Segmentation of thyroid nodules using Improvised U-Net Architecture

2020 ◽  
Vol 9 (8) ◽  
pp. 56-60
Keyword(s):  
Thyroid Nodules ◽  
Performance Metrics ◽  
Automatic Segmentation ◽  
Research Work ◽  
Ultrasound Image ◽  
True Positive Rate ◽  
Medical Image Segmentation ◽  
Common Disease ◽  
Low Resolution ◽  
Feature Map

Thyroid nodules are considered as most common disease found in adults and thyroid cancer has increased over the years rapidly. Further automatic segmentation for ultrasound image is quite difficult due to the image poor quality, hence several researcher have focused and observed that U-Net achieves significant performance in medical image segmentation. However U-net faces the problem of low resolution which causes smoothness in image, hence in this research work we have proposed improvised U-Net which helps in achieving the better performance. The main aim of this research work is to achieve the probable Region of Interest through segmentation with better efficiency. In order to achieve that Improvised U-Net develops two distinctive feature map i.e. High level feature Map and low level feature map to avoid the problem of low resolution. Further proposed model is evaluated considering the standard dataset based on performance metrics such as Dice Coefficient and True positive Rate. Moreover our model achieves better performance than the existing model.

Sign in / Sign up

Export Citation Format

Share Document