Skip to main content

Computer’s help in wound image analysis

Computers help in wound image analysis

CIALAB’s work in collaboration with the OSUMC Wound Center researchers entitled, “Computerized Segmentation and Measurement of Chronic Wound Images” has been accepted to the journal of Computers in Biology and Medicine (CBM). The objective of this study is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians.
Further details about the paper can be found at the CBM website: www.computersinbiologyandmedicine.com/article/S0010-4825(15)00064-5/abstract


Comments

  1. AnonymousMarch 24, 2019 at 8:41 PM

    Here is a decent discourse of some extraordinary thoughts. We should peruse these kinds of web journals for the improvement of our insight. All check this out understudies will appreciate this blog. We can impart this blog to an ever increasing number of individuals for the advancement of this blog which is extremely a decent blog.

    ReplyDelete
  2. AnonymousMarch 24, 2019 at 9:59 PM

    The method for Chinese Lunar New Year Celebration is introduced through this blog. We can get inspiration from this blog. This inspiration would be helpful for read this article tests. I am functioning as an essayist and I discovered this blog so much intrigued. I should state this is a best blog from the various sites.

    ReplyDelete
  3. AnonymousMarch 24, 2019 at 10:34 PM

    We imagine that we are not consider the working of the creator. We might attempt get some more website data since he doesn't know from this blog. Expectation you can comprehend when you come here and begin to peruse or might be begin to saw these photographs.

    ReplyDelete

Post a Comment

Popular posts from this blog

Recent publications

The CIA lab has recently had 4 articles published in PLOS One and the Journal of Urology. Automated Staging Of T1 Bladder Cancer Using Digital Pathologic H&E Images: A Deep Learning approach (Journal of Urology). The paper discusses the need for accurately gauging tumor cell intrusion into Lamina Propria in an effort to substage bladder cancer. It explains how transfer learning in conjunction with Convolutional Neural Networks can be used to accurately identify different bladder layers and then compute the distance between tumor nuclei and Lamina Propria. The article is available here:  https://www.jurology.com/article/S0022-5347(18)41148-2/pdf Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning (PLOS One). This paper examines a proposed methodology to automatically differentiate between NET and non-tumor regions based on images of Ki67 stained biopsies. It also uses transfer learning to exploit a rich set of features ...
6 comments
Read more

Drs. Goceri, Kus, and Senaras Present at OSUMC Research Day

On April 25th, Drs. Goceri, Kus, and Senaras presented their research at the OSUMC Research Day.  Dr. Evgin Goceri presented “Automatic and Robust Segmentation of Liver and Its Vessels from MR Datasets for Pre-Evaluation of Liver Translation” which proposed a robust and fully automated method for segmenting the liver and its vessels from MR images. Dr. Goceri’s study presented a novel approach to reducing processing time by employing binary regularization of the level set function. The fully-automatic segmentation of liver and its vessels with the proposed method was more efficient than manual approach and the other methods in the literature in terms of processing time and accuracy. Dr. Pelin Kus presented “Segmentation and Quantification of Tissue Necrosis in Tuberculosis” which focuses on how the immune system of patients infected with M. tuberculosis responds by using many types of cells including macrophages that form granulomas within the pulmonary tissue. Segmentati...
3 comments
Read more