Automatic non-parametric capsid segmentation using wavelets transform and graph

Cassany, Aurélia; Kann, Michael; Sibarita, Jean‐Baptiste

doi:10.1109/isbi.2012.6235822

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Low (poor) contrast ratio between background and foreground, is also a big challenge faced in microorganism images [55], in which histogram of images have no clear defined valley point for thresholding. This challenge mostly appears to images from conventional light microscopes which are commonly used in many third world countries [40], [45], however, this can be resolved using iterative thresholding [124], wavelet transform [55] and color based thresholding. A clear example can be seen in [45] where color and global adaptive thresholding are used to threshold low contrast image from conventional microscope.…”

Section: A Analysis On Classical Methodsmentioning

confidence: 99%

“…This is why they are mostly used [125]. Furthermore, they don't require many datasets, thus suitable for segmentation of medical related microorganisms where confidentiality leads to scarcity of dataset [45], [46], [55]. On top of that, threshold based methods have simple algorithms which can be run by low speed computers and are incorporated in software libraries which can be utilized by almost all common programming languages and platforms, such as python and Matlab, contrary to many machine learning models which need high speeds, high number of dataset and can run well only in some special IDEs.…”

Section: A Analysis On Classical Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Kulwa

Zhao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium. In such course, it is important to identify, track, analyze, consider the beneficial side and get rid of the negative effects of microorganisms using fast, accurate, and reliable methods. In recent decades, image analysis techniques have been used to address the drawbacks of manual traditional approaches in the identification and analysis of microorganisms. As image segmentation being an important step (technique) in the detection, tracking, monitoring, feature extraction, modeling, and analysis of microorganisms, different methods have been deployed, from classical approaches to current deep neural networks upon different challenges on microorganism images. This survey comprehensively analyses the various studies focused on developing microorganism image segmentation methods in the last 30 years (since 1989). In this survey, segmentation methods are categorized into classical and machine learning methods. Furthermore, these methods are subcategorized into threshold-based, regionbased, and edge-based which belong to classical methods, supervised and unsupervised machine leaningbased methods which belong to machine learning category. A growth trend of different methods and most frequently used methods in each category are meticulously analyzed. A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened. INDEX TERMSMicroorganism segmentation, content-based microscopic image analysis, feature extraction, microscopic images, classical methods, machine learning.

show abstract