Diminution index: A novel 3D feature for pulmonary nodule detection

Matsumoto, Shinsuke; Ohno, Yoshiharu; Yamagata, Hitoshi; Asahina, Hiroshi; Komatsu, Kenichi; Sugimura, Kazuro

doi:10.1016/j.ics.2005.03.153

Cited by 2 publications

(5 citation statements)

References 3 publications

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“…After this analysis, only 38 articles [24,4,18,22,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58] were identified as 3D automated algorithms to segment lung nodules in CT images, the target of this work.…”

Section: Work Selection Criteriamentioning

confidence: 99%

“…This stage is important since the lungs also contain several structures that can be confused with nodules. Just eight of the selected works for this review indicate the preprocessing techniques used: linear interpolation [26,29,46], median filter [27,28], morphological Hat operation [28], Gaussian filter [32,52] and weighted-sum filter [51].…”

Section: Preprocessingmentioning

confidence: 99%

“…Matsumoto et al [46] defined a new 3D feature called diminution index that is able to differentiate vessels and pulmonary nodules attached to vessels. The algorithm was trained with 4 exams and 30 nodules.…”

Section: Selected Workmentioning

confidence: 99%

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Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: Work Selection Criteriamentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

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Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

204

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“…The diminution index of the nodule candidate is then defi ned to take the value given by one minus the ratio of the volume of the distal part to that of the proximal part. 8 As is obvious from this defi nition, the diminution index provides a measure that is independent of the absolute size of the nodule candidate. Thus, the following characteristics of the diminution index, which are highly suitable for CAD of lung nodules on CT, hold for nodule candidates of any size: The values of the diminution index are close to 1 for nodule candidates representing isolated nodules and generally around 0 or lower for nodule candidates caused by blood vessels; as compared with these two cases, the values of the diminution index are in an intermediate range for nodule candidates representing juxtavascular nodules.…”

Section: Patients and Ct Image Databasementioning

confidence: 99%

“…The resultant summary measure of the 3D analysis of nodule candidates and their surroundings, called the diminution index, has shown promising capability in the classifi cation of nodule candidates. 8 We have been developing a CAD system for lung nodules on MDCT whose scheme for nodule detection integrates the above 3D analysis of nodule candidates and their surroundings with the preceding identifi cation of nodule candidates using deformable ellipsoid models. The purpose of this study was to conduct a preliminary evaluation of the CAD system.…”

Section: Introductionmentioning

confidence: 99%