Delineation and quantitation of brain lesions by fuzzy clustering in Positron Emission Tomography

Boudraa, Abdel-Ouahab; Champier, Jacques; Cinotti, L.; Bordet, Jean‐Claude; Lavenne, F.; Mallet, Jean-Jacques

doi:10.1016/0895-6111(96)00025-0

Cited by 33 publications

(22 citation statements)

References 21 publications

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“…However, the number of tumors segmented in the study was limited, and there was no clear consensus on the choice of similarity parameters, which might not be optimal when only intensity values are used as similarity parameters. Another common clustering method, FCM, was used in PET segmentation context first in [136], and it has been used mainly for PET brain lesion segmentation since [136, 137]. The FCM algorithm classifies voxels into one of two groups, based on “fuzzy” levels where, due to the low resolution and the PVE, a particular voxel is allowed to included a mixture of multiple tissue types.…”

Section: Stochastic and Learning-based Methodsmentioning

confidence: 99%

A review on segmentation of positron emission tomography images

Foster

Bağcı

Mansoor

et al. 2014

Computers in Biology and Medicine

324

290

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Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results.

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Section: Stochastic and Learning-based Methodsmentioning

confidence: 99%

A review on segmentation of positron emission tomography images

Foster

Bağcı

Mansoor

et al. 2014

Computers in Biology and Medicine

324

290

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“…6,9 Clustering approaches such as k-means (KM) or fuzzy C-means have also been implemented in segmenting nuclear medicine images. 10 K-means based methods segment PET images into two regions (lesion and background) by initially choosing a cluster center and then iterating to minimize the withincluster sum-of-squares. This approach tends to be sensitive to the initial choice of cluster center selection and noise in the image.…”

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confidence: 99%

Combining multiple FDG‐PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods

et al. 2013

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Purpose: Many approaches have been proposed to segment high uptake objects in 18F-fluoro-deoxyglucose positron emission tomography images but none provides consistent performance across the large variety of imaging situations. This study investigates the use of two methods of combining individual segmentation methods to reduce the impact of inconsistent performance of the individual methods: simple majority voting and probabilistic estimation. Methods: The National Electrical Manufacturers Association image quality phantom containing five glass spheres with diameters 13-37 mm and two irregularly shaped volumes (16 and 32 cc) formed by deforming high-density polyethylene bottles in a hot water bath were filled with 18-fluorodeoxyglucose and iodine contrast agent. Repeated 5-min positron emission tomography (PET) images were acquired at 4:1 and 8:1 object-to-background contrasts for spherical objects and 4.5:1 and 9:1 for irregular objects. Five individual methods were used to segment each object: 40% thresholding, adaptive thresholding, k-means clustering, seeded region-growing, and a gradient based method. Volumes were combined using a majority vote (MJV) or Simultaneous Truth And Performance Level Estimate (STAPLE) method. Accuracy of segmentations relative to CT ground truth volumes were assessed using the Dice similarity coefficient (DSC) and the symmetric mean absolute surface distances (SMASDs). Results: MJV had median DSC values of 0.886 and 0.875; and SMASD of 0.52 and 0.71 mm for spheres and irregular shapes, respectively. STAPLE provided similar results with median DSC of 0.886 and 0.871; and median SMASD of 0.50 and 0.72 mm for spheres and irregular shapes, respectively. STAPLE had significantly higher DSC and lower SMASD values than MJV for spheres (DSC, p < 0.0001; SMASD, p = 0.0101) but MJV had significantly higher DSC and lower SMASD values compared to STAPLE for irregular shapes (DSC, p < 0.0001; SMASD, p = 0.0027). DSC was not significantly different between 128 × 128 and 256 × 256 grid sizes for either method (MJV, p = 0.0519; STAPLE, p = 0.5672) but was for SMASD values (MJV, p < 0.0001; STAPLE, p = 0.0164). The best individual method varied depending on object characteristics. However, both MJV and STAPLE provided essentially equivalent accuracy to using the best independent method in every situation, with mean differences in DSC of 0.01-0.03, and 0.05-0.12 mm for SMASD. Conclusions: Combining segmentations offers a robust approach to object segmentation in PET. Both MJV and STAPLE improved accuracy and were robust against the widely varying performance of individual segmentation methods. Differences between MJV and STAPLE are such that either offers good performance when combining volumes. Neither method requires a training dataset but MJV is simpler to interpret, easy to implement and fast.

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“…Although this non local prior showed better performance compared to conventional prior, it required the computationally intense approach of re evaluating weights at every iteration. In the different context of post-reconstruction PET/SPECT image analysis, a number of clustering-based techniques were previously proposed to better facilitate segmentation [8]- [12]. We think the clustering based techniques can be used for prior definition In this work, the fuzzy C-means (FCM) algorithm was used to group voxels into clusters, as initially obtained using initial reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Cluster-based priors for MAP PET image reconstruction

Tang

Karakatsanis

et al. 2011

2011 IEEE Nuclear Science Symposium Conference Record

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We propose two forms of cluster-based priors for the maximum a Posterior (MAP) algorithm to improve PET image re construction quantitatively. Conventionally, most priors in MAP reconstruction use weighted differences between voxel intensities within a small localized spatial neighborhood, exploiting intensity similarities amongst adjacent voxels. It was hypothesized that by incorporating a larger collection of voxels with similar properties, the MAP approach has a greater ability to impose smoothness while preserving edges. We propose to use clustering techniques as applied to pre-reconstructed images to define clustered neigh borhoods of voxels with similar intensities. Two forms of cluster based priors were proposed. The unweighted cluster-based prior (CP-U) applies a uniform weight regardless of position within a cluster to voxel value differences. The distance weighted cluster based prior (CP-W) applies different weights based on the distance between voxels within a cluster. The two forms of cluster based priors, CP-U and CP-W, are implemented within MAP reconstruction. The fuzzy C-means (FCM) method is used to cluster the filtered backprojection (FBP) reconstructed image before MAP reconstruction. To evaluate the proposed priors, a mathematical brain phantom was used in analytic simulations to generate the projection data. We compare reconstructed images from the proposed cluster-based priors MAP algorithms with those from conventional MLEM and quadratic prior (QP) MAP algorithms, using the regional bias (normalized mean squared error, NMSE) vs noise (normalized standard deviation tradeoff, NSD) tradeoff curves. MAP reconstruction using cluster-based priors (CP-U-MAP and CP-W-MAP) dramatically improved the noise vs. bias tradeoff when the number of clusters selected is equal to or larger than the true number of clusters within the image. However, the CP-U-MAP may introduce some bias in a region that may be wrongly clustered, e.g. when the number of selected clusters is smaller than the true number of clusters, a problem that is largely avoided by CP-W-MAP reconstruction which exhibits very robust quantitative performance.

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Delineation and quantitation of brain lesions by fuzzy clustering in Positron Emission Tomography

Cited by 33 publications

References 21 publications

A review on segmentation of positron emission tomography images

A review on segmentation of positron emission tomography images

Combining multiple FDG‐PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods

Cluster-based priors for MAP PET image reconstruction

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