AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle

Sapkota, Manish; Liu, Fujun; Xie, Yuanpu; Su, Hua; Xing, Fuyong; Yang, Lin

doi:10.1109/jbhi.2017.2694344

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Digital filters are useful for image pre-processing tasks. A variety of filters have been used in the literature to extract the properties of images [25][26][27][28][29]. Prewitt operators are used to detecting edges along the x and y directions to yield masks G x and G y , respectively.…”

Section: Feature Extraction 351 Prewitt Edge Detection Filtermentioning

confidence: 99%

Pixel-Level Feature Extraction Model for Breast Cancer Detection

Behar¹,

Shrivastava²

2023

Computers, Materials &Amp; Continua

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Breast cancer is the most prevalent cancer among women, and diagnosing it early is vital for successful treatment. The examination of images captured during biopsies plays an important role in determining whether a patient has cancer or not. However, the stochastic patterns, varying intensities of colors, and the large sizes of these images make it challenging to identify and mark malignant regions in them. Against this backdrop, this study proposes an approach to the pixel categorization based on the genetic algorithm (GA) and principal component analysis (PCA). The spatial features of the images were extracted using various filters, and the most prevalent ones are selected using the GA and fed into the classifiers for pixel-level categorization. Three classifiers-random forest (RF), decision tree (DT), and extra tree (ET)were used in the proposed model. The parameters of all models were separately tuned, and their performance was tested. The results show that the features extracted by using the GA+PCA in the proposed model are influential and reliable for pixel-level classification in service of the image annotation and tumor identification. Further, an image from benign, malignant, and normal classes was randomly selected and used to test the proposed model. The proposed model GA-PCA-DT has delivered accuracies between 0.99 to 1.0 on a reduced feature set. The predicted pixel sets were also compared with their respective ground-truth values to assess the overall performance of the method on two metrics-the universal image quality index (UIQI) and the structural similarity index (SSI). Both quality measures delivered excellent results.

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Section: Feature Extraction 351 Prewitt Edge Detection Filtermentioning

confidence: 99%

Pixel-Level Feature Extraction Model for Breast Cancer Detection

Behar¹,

Shrivastava²

2023

Computers, Materials &Amp; Continua

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“…Unfortunately, the process of manual interpretation is time consuming, challenging and error-prone due to their large size and inter/intra-observer variation [1]- [3]. To address these problems and improve the efficiency of the procedure, different content-based image retrieval (CBIR) methods for automated computer aided diagnosis (CAD) have been proposed [4]- [10]. CBIR aims to directly utilize a visual query to find the most similar cases (nearest neighbors) among the images previously diagnosed and stored in databases.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images

Sapkota

Shi

Xing

et al. 2019

IEEE J. Biomed. Health Inform.

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Compact binary representations of histopathology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality of the query image based on the retrieved similar cases, thereby providing support for medical diagnosis. They also allow for efficient managing of large-scale image databases because of a low storage requirement. However, the effectiveness of binary representations heavily relies on the visual descriptors that represent the semantic information in the histopathological images. Traditional approaches with hand-crafted visual descriptors might fail due to significant variations in image appearance. Recently, deep learning architectures provide promising solutions to address this problem using effective semantic representations. In this paper, we propose a Deep Convolutional Hashing (DCH) method that can be trained "point-wise" to simultaneously learn both semantic and binary representations of histopathological images. Specifically, we propose a convolutional neural network (CNN) that introduces a latent binary encoding (LBE) layer for low dimensional feature embedding to learn binary codes. We design a joint optimization objective function that encourages the network to learn discriminative representations from the label information, and reduce the gap between the real-valued low dimensional embedded features and desired binary values. The binary encoding for new images can be obtained by forward propagating through the network and quantizing the output of the LBE layer. Experimental results on a large-scale histopathological image dataset demonstrate the effectiveness of the proposed method.

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