Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications

• URL: http://arxiv.org/abs/2201.11987v2
• Date: Mon, 31 Jan 2022 01:26:18 GMT
• Title: Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications
• Abstract summary: It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance of the scaffold. It is difficult using a single traditional image processing algorithm to extract continuous and accurate contour of a porous bioelastomer. This paper proposes a joint algorithm for the bioelastomer's contour detection and a texture feature extraction method for monitoring the degradation behavior of the bioelastomer.
• Score: 3.4276717980242553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance of the scaffold, not only to obviate the need to do destructive testing, but also to monitor the scaffold's degradation and tissue ingrowth over time. It is difficult using a single traditional image processing algorithm to extract continuous and accurate contour of a porous bioelastomer. This paper proposes a joint algorithm for the bioelastomer's contour detection and a texture feature extraction method for monitoring the degradation behavior of the bioelastomer. Mean-shift clustering method is used to obtain the bioelastomer's and native tissue's clustering feature information. Then the OTSU image binarization method automatically selects the optimal threshold value to convert the grayscale ultrasound image into a binary image. The Canny edge detector is used to extract the complete bioelastomer's contour. The first-order and second-order statistical features of texture are extracted. The proposed joint algorithm not only achieves the ideal extraction of the bioelastomer's contours in ultrasound images, but also gives valuable feedback of the degradation behavior of the bioelastomer at the implant site based on the changes of texture characteristics and contour area. The preliminary results of this study suggest that the proposed computer-aided image processing techniques have values and potentials in the non-invasive analysis of tissue scaffolds in vivo based on ultrasound images and may help tissue engineers evaluate the tissue scaffold's degradation and cellular ingrowth progress and improve the scaffold designs.

Related papers

• Novel computational workflows for natural and biomedical image processing based on hypercomplex algebras [49.81327385913137]
  Hypercomplex image processing extends conventional techniques in a unified paradigm encompassing algebraic and geometric principles. This workleverages quaternions and the two-dimensional planes split framework (splitting of a quaternion - representing a pixel - into pairs of 2D planes) for natural/biomedical image analysis. The proposed can regulate color appearance (e.g. with alternative renditions and grayscale conversion) and image contrast, be part of automated image processing pipelines.
  arXiv  Detail & Related papers  (2025-02-11T18:38:02Z)
• Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping [3.550171634694342]
  Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude.
  arXiv  Detail & Related papers  (2024-02-28T00:57:35Z)
• Automated segmentation and morphological characterization of placental histology images based on a single labeled image [0.0]
  A novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. A diversified artificial dataset of images is generated that can be used for training deep learning segmentation models.
  arXiv  Detail & Related papers  (2022-10-07T14:00:10Z)
• Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma [2.8265965924600276]
  This study aims to use artificial intelligence to classify osteosarcoma histological images and to assess tumor survival and necrosis. We propose a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning. Our method outperforms the traditional and deep learning approaches on various evaluation metrics, with an accuracy of 99.17% to support osteosarcoma diagnosis.
  arXiv  Detail & Related papers  (2022-04-29T00:57:39Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• Principal component-based image segmentation: a new approach to outline in vitro cell colonies [0.0]
  We present an objective and versatile machine learning procedure to amend issues by characterizing, extracting and segmenting inquired colonies. The proposed segmentation algorithm yielded a similar quality as manual counting by human observers.
  arXiv  Detail & Related papers  (2021-03-10T12:37:51Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames [146.63177174491082]
  Fuzzy $C$-Means (FCM) algorithm incorporates a morphological reconstruction operation and a tight wavelet frame transform. We present an improved FCM algorithm by imposing an $ell_0$ regularization term on the residual between the feature set and its ideal value. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.
  arXiv  Detail & Related papers  (2020-02-14T10:00:03Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.