Algorithm Selection Based on a Region Similarity Metric for Intracellular Image Segmentation

Takemoto, Satoko; Yokota, Hideo

doi:10.5772/15807

Cited by 6 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This initial work provides an opportunity for building a fully developed reference database of sensitivity signatures of similarity metrics, for providing selection recommendations of similarity metrics driven by application specific requirements, and for eventually delivering web-based image comparison services. similarity computations are frequently researched in segmentation papers [10][11][12][13][14][15]. For example, Zhang et al [12] focused on unsupervised segmentation methods that depend on the choice of similarity metrics.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, there is a lack of understanding on how to map application requirements to specific computations of similarity metrics. While there are many papers proposing new similarity metrics [7], [8], [13], the lack of a recommendation system for choosing a similarity metric according to application specific requirements motivates our work.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toward a Recommendation System for Image Similarity Metrics

Bajcsy¹,

Chalfoun²,

Brady³

2012

Imaging and Signal Processing in Health Care and Technology / 772: Human-Computer Interaction / 773: Communication, Internet An

View full text Add to dashboard Cite

This paper addresses the problem of mapping application specific requirements on image similarity metrics to the plethora of existing image similarity computations. The work is motivated by the fact that there is no method for choosing a similarity metric that is suitable for a given application. We approached the problem by designing a theoretical and experimental framework for creating sensitivity signatures of similarity metrics. In this paper, we outline the classifications of image similarity metrics found in the literature, the space of application parameters and requirements, derivations of similarity dependencies on application parameters, and experimentally obtained sensitivity signatures of similarity metrics using image simulations. These sensitivity signatures provide a way for users to query a reference database of sensitivity signatures and retrieve a recommendation for an image similarity metric. We illustrate the use of the prototype recommendation system by considering spectral calibration and spatial registration application requirements.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward a Recommendation System for Image Similarity Metrics

Bajcsy¹,

Chalfoun²,

Brady³

2012

Imaging and Signal Processing in Health Care and Technology / 772: Human-Computer Interaction / 773: Communication, Internet An

View full text Add to dashboard Cite

show abstract

“…The overlap and object geometry based similarity computations are frequently researched in segmentation papers [10][11][12][13][14][15]. The similarity metrics are classified according to segmentation use as intra-region, interregion and composite pixel comparisons, and include considerations of texture, entropy and various color descriptors.…”

Section: Introductionmentioning

confidence: 99%

“…The literature survey indicates a lack of understanding in mapping application requirements of image processing tasks to the most appropriate similarity metrics for a given task. While there are many papers proposing new similarity metrics [7], [8], [13], there is no systematic framework for choosing a similarity metric according to application specific requirements other than our initial work [17]..…”

Section: Introductionmentioning

confidence: 99%

How to select microscopy image similarity metrics?

Bajcsy¹,

Chalfoun²,

Brady³

2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

View full text Add to dashboard Cite

Comparisons of two microscopy images can be accomplished in many different ways. This paper presents a system that recommends appropriate similarity metrics for microscopy image comparisons based on biological application requirements. The motivation stems from the fact that task requirements can drive the automatic selection of a similarity metric. The suitability of a particular image similarity metric is modeled as the sensitivity and invariance of the metric to microscopy image content and the associated dynamic changes of this content.. In this paper, we describe a mathematical and experimental basis of an image similarity metric recommendation system. In this system, we build a database of sensitivity signatures, and query this reference database to retrieve a similarity metric based on given biological requirements. We illustrate a prototype recommendation system based on synthetic and measured images for spectral calibration and spatial registration applications.

show abstract

Region based image segmentation to improve accuracy of currency images compared with edge based segmentation

Reddy,

Kumar

2024

Fifth International Conference on Applied Sciences: Icas2023

View full text Add to dashboard Cite

Algorithm Selection Based on a Region Similarity Metric for Intracellular Image Segmentation

Cited by 6 publications

References 28 publications

Toward a Recommendation System for Image Similarity Metrics

Toward a Recommendation System for Image Similarity Metrics

How to select microscopy image similarity metrics?

Region based image segmentation to improve accuracy of currency images compared with edge based segmentation

Contact Info

Product

Resources

About