Nonlinear adaptive convergence index filters and their characteristics

Kobatake, Hidefumi; Wei, Jun; Yoshinaga, Yukiyasu; Hagihara, Yukari; Shimizu, A.

doi:10.1109/icpr.2000.903598

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…A 3D spatial filter called adaptive convergence index filter [11] is employed to enhance the cancers in the liver region. It calculates a convergence index of gradient vectors in the spherical region of Fig.…”

Section: Enhancement Of Cancersmentioning

confidence: 99%

Proposal of computer-aided detection system for three dimensional CT images of liver cancer

Shimizu

Kawamura

Kobatake

2005

International Congress Series

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Section: Enhancement Of Cancersmentioning

confidence: 99%

Proposal of computer-aided detection system for three dimensional CT images of liver cancer

Shimizu

Kawamura

Kobatake

2005

International Congress Series

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“…The characteristics of typical filters of this kind and examples of their application are also described [15,16]. In particular, the responses of the convergence index filter to 3D images, as well as the relations among filters, which have not previously been analyzed, are presented.…”

Section: Introductionmentioning

confidence: 99%

A convergence index filter for vector fields and its application to medical image processing

Kobatake

2006

Electron. Comm. Jpn. Pt. III

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SUMMARYIt often happens in medical image processing that shadows with very weak contrast must be handled. When an ordinary difference filter is applied, shadows with a strong contrast other than the target shadow give a disturbance signal, making it difficult to extract the desired region embedded in the disturbance-producing shadows. This paper notes the directions of the gradient vector in the image and discusses a filter which can detect objects with high sensitivity based on the direction distribution. In this paper, the convergence index of the vector is generally defined, and a general expression for the convergence index filter which can be applied to both 2D and 3D images is presented. Specifically, the iris filter and the line convergence index filter are considered, and their responses are analyzed, together with their relations. An application to the actual medical image is presented, demonstrating the effectiveness of the idea.

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“…It should be noted that there are liver tumors of various sizes and with various ranges of contrast with the background. The 3D adaptive convergence index filter [7] is used, since it can evaluate the convergence of the density gradient vectors, a feature of the tumors, without being greatly affected by these differences. The local maximum points are detected from the enhanced image, and then the tumor candidate regions are extracted by applying region-growing method to the original image.…”

Section: Introductionmentioning

confidence: 99%

Development of an automated extraction method for liver tumors in three‐dimensional multiphase multislice images

Nakagawa

Shimizu

Kobatake

2005

Systems & Computers in Japan

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SUMMARYThis paper proposes an automatic method for extracting liver tumors from three-dimensional abdominal CT images of four phases (noncontrast, early phase, portal phase, late phase). In the proposed method, the liver region is extracted from the image of each phase. Enhancement process is applied to the region by using a three-dimensional adaptive convergence index filter. The local maximum points are extracted from the enhanced image and the candidate regions for the liver tumor are extracted by applying the region-growing process. Multiple features are derived from the extracted candidates and it is decided whether or not the candidate actually corresponds to a tumor, based on the Mahalanobis distance calculated from the features. The processing from the enhancement to the discrimination of the candidates is performed independently for each phase, and four processed images are generated. Finally, the resultant images are combined and output. The proposed method is applied to 40 three-dimensional CT images of 10 patients taken by a multislice CT scanner. The result is promising, since all 8 existing tumors were detected, and the number of false-positive regions per image was 0.

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Nonlinear adaptive convergence index filters and their characteristics

Cited by 5 publications

References 3 publications

Proposal of computer-aided detection system for three dimensional CT images of liver cancer

Proposal of computer-aided detection system for three dimensional CT images of liver cancer

A convergence index filter for vector fields and its application to medical image processing

Development of an automated extraction method for liver tumors in three‐dimensional multiphase multislice images

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