Fuzzy Distance Transform: Theory, Algorithms, and Applications

Saha, Punam K.; Wehrli, Félix W.; Gomberg, Bryon R.

doi:10.1006/cviu.2002.0974

Cited by 141 publications

(104 citation statements)

References 36 publications

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“…Therefore, the distribution of regional thickness over an object is calculated by sampling the FDT values along its skeleton. Details of the algorithm are given by Saha et al (134) and its application to TB and validation for accuracy and precision is described by Saha and Wehrli (129). This work shows that highly accurate thickness measurements can be obtained at voxel sizes on the order of actual TB thickness and in the presence of SNR values encountered in vivo.…”

Section: D Structure Analysismentioning

confidence: 99%

Quantitative MRI for the assessment of bone structure and function

et al. 2006

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Osteoporosis is the most common degenerative disease in the elderly. It is characterized by low bone mass and structural deterioration of bone tissue, leading to morbidity and increased fracture risk in the hip, spine and wrist-all sites of predominantly trabecular bone. Bone densitometry, currently the standard methodology for diagnosis and treatment monitoring, has significant limitations in that it cannot provide information on the structural manifestations of the disease. Recent advances in imaging, in particular MRI, can now provide detailed insight into the architectural consequences of disease progression and regression in response to treatment. The focus of this review is on the emerging methodology of quantitative MRI for the assessment of structure and function of trabecular bone. During the past 10 years, various approaches have been explored for obtaining image-based quantitative information on trabecular architecture. Indirect methods that do not require resolution on the scale of individual trabeculae and therefore can be practiced at any skeletal location, make use of the induced magnetic fields in the intertrabecular space. These fields, which have their origin in the greater diamagnetism of bone relative to surrounding marrow, can be measured in various ways, most typically in the form of R 0 2 , the recoverable component of the total transverse relaxation rate. Alternatively, the trabecular network can be quantified by high-resolution MRI (m-MRI), which requires resolution adequate to at least partially resolve individual trabeculae. Micro-MRI-based structure analysis is therefore technically demanding in terms of image acquisition and algorithms needed to extract the structural information under conditions of limited signal-to-noise ratio and resolution. Other requirements that must be met include motion correction and image registration, both critical for achieving the reproducibility needed in repeat studies. Key clinical applications targeted involve fracture risk prediction and evaluation of the effect of therapeutic intervention.

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Section: D Structure Analysismentioning

confidence: 99%

Quantitative MRI for the assessment of bone structure and function

et al. 2006

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“…In first step original CT image is taken as input and converted into fuzzy distance transform(FDT) image. One can find a vast and rich literature on FDT and its application [11], [12], [13]. Recently Guha et al proposed a FDT based geodesic path propagation algorithm for reconstruction of cerebrovascular phantom which required least user interaction [5].…”

Section: Design Of Some Cerebrovascular Structure With and Without Anmentioning

confidence: 99%

Classification of anomalous region in human cerebrovascular structure using random forest

2018

IJLTET

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“…. , c l 〉 in C, wherein the consecutive spels are 26-adjacent, we define its fuzzy length as is interpreted as an average bone density of the link 〈c i , c i+1 〉, then π B (p) is approximately the total bone mass of p.) The fuzzy distance transform [23] is derived from f B as follows:…”

Section: Qualitative Evaluationmentioning

confidence: 99%

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Ciesielski

Udupa

Saha

et al. 2007

Computer Vision and Image Understanding

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In this paper we present a new theory and an algorithm for image segmentation based on a strength of connectedness between every pair of image elements. The object definition used in the segmentation algorithm utilizes the notion of iterative relative fuzzy connectedness, IRFC. In previously published research, the IRFC theory was developed only for the case when the segmentation was involved with just two segments, an object and a background, and each of the segments was indicated by a single seed. (See Udupa, Saha, Lotufo [15] and Saha, Udupa [14].) Our theory, which solves a problem of Udupa and Saha from [13], allows simultaneous segmentation involving an arbitrary number of objects. Moreover, each segment can be indicated by more than one seed, which is often more natural and easier than a single seed object identification.The first iteration step of the IRFC algorithm gives a segmentation known as relative fuzzy connectedness, RFC, segmentation. Thus, the IRFC technique is an extension of the RFC method. Although the RFC theory, due to Saha and Udupa [19], is developed in the multi object/multi seed framework, the theoretical results presented here are considerably more delicate in nature and do not use the results from [19]. On the other hand, the theoretical results from [19] are immediate consequences of the results presented here. Moreover, the new framework not only subsumes previous fuzzy connectedness descriptions but also sheds new light on them. Thus, there are fundamental theoretical advances made in this paper.We present examples of segmentations obtained via our IRFC based algorithm in the multi object/ multi seed environment, and compare it with the results obtained with the RFC based algorithm. Our results indicate that, in many situations, IRFC outperforms RFC, but there also exist instances where the gain in performance is negligible.

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Fuzzy Distance Transform: Theory, Algorithms, and Applications

Cited by 141 publications

References 36 publications

Quantitative MRI for the assessment of bone structure and function

Quantitative MRI for the assessment of bone structure and function

Classification of anomalous region in human cerebrovascular structure using random forest

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

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