FPGA-based acceleration of mutual information calculation for real-time 3D image registration

Castro-Pareja, Carlos R.; Jagadeesh, J.M.; Shekhar, Raj

doi:10.1117/12.525412

Cited by 19 publications

(41 citation statements)

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“…Figure 4.14 shows a plot of the error magnitude vs. p for both these realizations. The maximum error was of the order of 10 -8 for a both these configurations, which is sufficient for MI calculation in the context of image registration [155]. For our architecture we selected realization with 8K entries, as it offers superior error performance at a relatively moderate increase in the LUT size.…”

Section: Multiple Lut-based Approach For Entropy Calculationmentioning

confidence: 99%

“…The multiple LUT-based approach introduced by Castro-Pareja et al [155] attempts to address these limitations. In this approach, each segment is stored in a different LUT.…”

Section: Multiple Lut-based Approach For Entropy Calculationmentioning

confidence: 99%

“…However, this module has already been optimized for accuracy and hardware resources, as described in [155]. The optimization strategy employed in [155] uses an analytical approach that is specific to entropy calculation and is distinct from the strategy presented in this work.…”

Section: Mutual Histogram Accumulationmentioning

confidence: 99%

“…Furthermore, this method provides a realization that is optimized for approximation accuracy and hardware resources [155].…”

Section: Multiple Lut-based Approach For Entropy Calculationmentioning

confidence: 99%

“…The mutual and individual entropies are computed by using the probability distributions represented by the mutual and individual histograms, respectively. [155]. This approach minimizes the error in representing ( ) f p for a given number and size of LUTs and, hence, is accurate and efficient.…”

Section: Entropy Calculationmentioning

confidence: 99%

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High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Dandekar¹

2008

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Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissues, and lead to faster patient recovery. Advanced three-dimensional (3D) image processing is a critical need for navigation during IGIs. However, achieving on-demand performance, as required by IGIs, for these image processing operations using software-only implementations is challenging because of the sheer size of the 3D images, and memory and compute intensive nature of the operations. This dissertation, therefore, is geared toward developing high-performance 3D image processing architectures, which will enable improved intraprocedural visualization and navigation capabilities during IGIs.In this dissertation we present an architecture for real-time implementation of 3D filtering operations that are commonly employed for preprocessing of medical images. This architecture is approximately two orders of magnitude faster than corresponding software implementations and is capable of processing 3D medical images at their acquisition speeds. Report Documentation PageCombining complementary information through registration between pre-and intraprocedural images is a fundamental need in the IGI workflow. Intensity-based deformable registration, which is completely automatic and locally accurate, is a promising approach to achieve this alignment. These algorithms, however, are extremely compute intensive, which has prevented their clinical use. We present an FPGA-based architecture for accelerated implementation of intensity-based deformable image registration. This high-performance architecture achieves over an order of magnitude speedup when compared with a corresponding software implementation and reduces the execution time of deformable registration from hours to minutes while offering comparable image registration accuracy.Furthermore, we present a framework for multiobjective optimization of finite-precision implementations of signal processing algorithms that takes into account multiple conflicting objectives such as implementation accuracy and hardware resource consumption. The evaluation that we have performed in the context of FPGA-based image registration demonstrates that such an analysis can be used to enhance automated hardware design processes, and efficiently identify a system configuration that meets given design constraints. In addition, we also outline two novel clinical applications that can directly benefit from these developments and demonstrate the feasibility of our approach in the context of these applications. These advances will ultimately enable integration of 3D image processing into clinical workflow.

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Section: Multiple Lut-based Approach For Entropy Calculationmentioning

confidence: 99%

“…The multiple LUT-based approach introduced by Castro-Pareja et al [155] attempts to address these limitations. In this approach, each segment is stored in a different LUT.…”

Section: Multiple Lut-based Approach For Entropy Calculationmentioning

confidence: 99%