Automatic segmentation and recognition of lungs and lesion from CT scans of thorax

Kakar, Manish; Olsen, Dag Rune

doi:10.1016/j.compmedimag.2008.10.009

Cited by 57 publications

(43 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM and SVR are widely used in bioinformatics, [38][39][40] image processing, [41][42][43] and control systems. 44 SVMs are a general class of supervised learning methods that can perform classification or regression through mapping data into a higher dimension kernel space.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Identifying Factors Controlling Protein Release from Combinatorial Biomaterial Libraries via Hybrid Data Mining Methods

et al. 2010

View full text Add to dashboard Cite

Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO-nonconjugated bonds, the number of aromatic rings, and the number of -CH 2-bonds. ABSTRACT: Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO-nonconjugated bonds, the number of aromatic rings, and the number of -CH 2 -bonds.

show abstract

Section: Support Vector Regressionmentioning

confidence: 99%

Identifying Factors Controlling Protein Release from Combinatorial Biomaterial Libraries via Hybrid Data Mining Methods

et al. 2010

View full text Add to dashboard Cite

show abstract

“…The majority of existing work focuses on segmentation on CT images using various classification techniques [1,2,3]. Our method is partially motivated by these approaches.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference

Song

Cai

Eberl

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Positron emission tomography -computed tomography (PET-CT) is now accepted as the best imaging technique to accurately stage lung cancer. The consistent and accurate interpretation of PET-CT images, however, is not a trivial task. We propose a discriminative, multi-level learning and inference method to automatically detect the pathological contexts in the thoracic PET-CT images, i.e. the primary tumor and its spatial relationships within the lung and mediastinum, and disease in regional lymph nodes. The detection results can also be used as features to retrieve similar images with previous diagnosis from an imaging database as a reference set to aid physicians in PET-CT scan interpretation. Our evaluation with clinical data from lung cancer patients suggests our approach is highly accurate.

show abstract

“…Numerical lung analysis: Lung segmentation; volumetric analysis; densitometry; fractal dimension estimation Lung segmentation: Methods of lung segmentation are well established [8][9][10]. The lung segmentation approach used in this study is described in detail in ref.…”

Section: Computed Tomography Imagingmentioning

confidence: 99%

Detailed investigation of volumetric analysis, densitometry, and fractal geometry based concepts in computer aided medical diagnoses

Obert¹,

Stamm²,

Goerner³

et al. 2016

Fractal Geometry and Nonlinear Anal in Med and Biol

View full text Add to dashboard Cite

Purpose: Automated image analysis tools have the potential to improve the objectivity of the diagnostic process. The study and improvement of the numerical methodologies behind these tools is, therefore, crucial. Volumetric, densitometric, and fractal analysis concepts were, thus, explored in the setting of computed tomography (CT) imaging of different lung morphologies. Material and methods:Thoracic CT scans were acquired for five sheep prior to and after smoke inhalation injury. Software was developed to segment the lungs from the digital image data and to estimate the morphometric parameters "volume", "Hounsfield unit-density" (HU), and "fractal dimension". These parameters were estimated for each scan, once from the complete dataset, covering the entire a range of -1000 to 399 HU, and once for 28 consecutive data subsets, with a width of 50 HU each. T-test statistics were used to investigate group differences "before" and "after" smoke inhalation, based on a 0.05 significance level.Results: For the complete data set, group differentiation into "before" and "after" smoke in-halation was feasible only with volumetric analysis. Analysis of 28 smaller HU subsets, on the other hand, allowed group differentiation with all three morphometric parameters. Conclusions:The analysis of small HU subsets can be helpful in differentiating groups and may be a useful approach for many image analysis projects.

show abstract

Automatic segmentation and recognition of lungs and lesion from CT scans of thorax

Cited by 57 publications

References 34 publications

Identifying Factors Controlling Protein Release from Combinatorial Biomaterial Libraries via Hybrid Data Mining Methods

Identifying Factors Controlling Protein Release from Combinatorial Biomaterial Libraries via Hybrid Data Mining Methods

Discriminative Pathological Context Detection in Thoracic Images Based on Multi-level Inference

Detailed investigation of volumetric analysis, densitometry, and fractal geometry based concepts in computer aided medical diagnoses

Contact Info

Product

Resources

About