Image-guided filtering for improving photoacoustic tomographic image reconstruction

Awasthi, Navchetan; Kalva, Sandeep Kumar; Pramanik, Manojit; Yalavarthy, Phaneendra K.

doi:10.1117/1.jbo.23.9.091413

Cited by 28 publications

(26 citation statements)

References 91 publications

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“…In PAT/PACT, the laser illuminates the sample homogeneously and single or multiple USTs are used to collect PA signals from around the sample [13]. Then, reconstruction algorithms are used to process the collected PA signals to provide cross-sectional images of the target [14,15]. On the other hand, in PAM, a confocal optical excitation and ultrasonic detection is used to provide higher resolution compromising the imaging depth (in comparison to PAT/PACT).…”

Section: Introductionmentioning

confidence: 99%

1064 nm acoustic resolution photoacoustic microscopy

Periyasamy

Das

Sharma

et al. 2019

Journal of Biophotonics

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Photoacoustic imaging is a noninvasive imaging technique having the advantages of high‐optical contrast and good acoustic resolution at improved imaging depths. Light transport in biological tissues is mainly characterized by strong optical scattering and absorption. Photoacoustic microscopy is capable of achieving high‐resolution images at greater depth compared to conventional optical microscopy methods. In this work, we have developed a high‐resolution, acoustic resolution photoacoustic microscopy (AR‐PAM) system in the near infra‐red (NIR) window II (NIR‐II, eg, 1064 nm) for deep tissue imaging. Higher imaging depth is achieved as the tissue scattering at 1064 nm is lesser compared to visible or near infrared window‐I (NIR‐I). Our developed system can provide a lateral resolution of 130 μm, axial resolution of 57 μm, and image up to 11 mm deep in biological tissues. This 1064‐AR‐PAM system was used for imaging sentinel lymph node and the lymph vessel in rat. Urinary bladder of rat filled with black ink was also imaged to validate the feasibility of the developed system to study deeply seated organs.

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Section: Introductionmentioning

confidence: 99%

1064 nm acoustic resolution photoacoustic microscopy

Periyasamy

Das

Sharma

et al. 2019

Journal of Biophotonics

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“…It has established itself as a state‐of‐the‐art method and has found applications in image fusion [11], edge aware smoothing, structure transferring [29], flash/no flash denoising [30], joint upsampling, dehazing [31] and detail enhancement [32]. Previously, GF has been used in photoacoustic imaging [33] to perform image fusion and enhance the performance of reconstruction algorithms [34]. In this work, GF has been used to enhance the OoF images in the focal stack and the OoF image ( x O ) itself is used as the guiding image.…”

Section: Methodsmentioning

confidence: 99%

Guided filter based image enhancement for focal error compensation in low cost automated histopathology microscopic system

Awasthi

Katare

Gorthi

et al. 2020

Journal of Biophotonics

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Low-cost automated histopathology microscopy systems usually suffer from optical imperfections, producing images that are slightly Out of Focus (OoF). In this work, a guided filter (GF) based image preprocessing is proposed for compensating focal errors and its efficacy is demonstrated on images of healthy and malaria infected red blood cells (h-RBCs and i-RBCs), and PAP smears. Since contrast enhancement has been widely used as an image preprocessing step for the analysis of histopathology images, a systematic comparison is made with six such prominently used methods, namely Contrast Limited Adaptive Histogram Equalization (CLAHE), RIQMC-based optimal histogram matching (ROHIM), modified L 0 , Morphological Varying(MV)-Bitonic filter, unsharp mask filter and joint bilateral filter. The images enhanced using GF approach lead to better segmentation accuracy (upto 50% improvement over native images) and visual quality compared to other approaches, without any change in the color tones. Thus, the proposed GF approach is a viable solution for rectifying the OoF microscopy images without the loss of the valuable diagnostic information presented by the color tone.

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“…Recently proposed guided filter based method combines the feature from two different reconstructions techniques and was shown to perform better as compared to the contemporary methods for photoacoustic imaging [29]. The cartoon of the fusion process for PA imaging is given in Fig.…”

Section: Image Guided Filter Based Fusionmentioning

confidence: 99%

“…For complete information the reader can refer to Ref. [29]. The modified guided filtering algorithm is given here (Algorithm-2) for completeness.…”

Section: Image Guided Filter Based Fusionmentioning

confidence: 99%

“…Especially in areas of multi-focus and multi-camera fusion, the use of fusion based methods have shown to produce superior results [28]. Recently introduced modified guided filtering [29] based fusion method in PA imaging was shown to improve the reconstructed images. It uses a set of regularization parameters, which need to be selected carefully, to obtain the final image output.…”

Section: Introductionmentioning

confidence: 99%

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PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics

Awasthi¹,

Prabhakar²,

Kalva

et al. 2019

Biomed. Opt. Express

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The methods available for solving the inverse problem of photoacoustic tomography promote only one feature-either being smooth or sharp-in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data for training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and in-vivo studies. The improvement obtained in the reconstructed images was as high as 95.49% in root mean square error and 7.77 dB in signal to noise ratio (SNR) in comparison to the guided filter approach. Also, it was demonstrated that the proposed deep fuse approach, trained on only blood vessel type images at measurement data SNR being 40 dB, was able to provide a generalization that can work across various noise levels in the measurement data, experimental setups as well as imaging objects.

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Image-guided filtering for improving photoacoustic tomographic image reconstruction

Cited by 28 publications

References 91 publications

1064 nm acoustic resolution photoacoustic microscopy

1064 nm acoustic resolution photoacoustic microscopy

Guided filter based image enhancement for focal error compensation in low cost automated histopathology microscopic system

PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics

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