Gaussian Hermite polynomial based lossless medical image compression

Kumar, S. N.; Ahilan, A.; Haridhas, Ajay Kumar; Sebastian, Jins

doi:10.1007/s00530-020-00689-y

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…Since anteroposterior hip radiographs can visualize lesions in the pelvic bone and both proximal femurs with low cost and effort, they are frequently used as the initial imaging test for patients complaining of hip and thigh discomfort. In addition, several image pre-processing stages have been adopted for hip radiographs to be better recognized by the AI model [ 15 – 18 , 29 ]. The images were normalised using the min-max technique to reduce deviations between images due to variations in radiation exposure [ 15 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…In addition, several image pre-processing stages have been adopted for hip radiographs to be better recognized by the AI model [ 15 – 18 , 29 ]. The images were normalised using the min-max technique to reduce deviations between images due to variations in radiation exposure [ 15 , 29 ]. Using the binarization process, the noise in the image was removed, and the boundary of the radiopaque area was clarified to identify the exact center of the body.…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation

Park

Kim

et al. 2022

PLoS ONE

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Purpose Early detection and classification of bone tumors in the proximal femur is crucial for their successful treatment. This study aimed to develop an artificial intelligence (AI) model to classify bone tumors in the proximal femur on plain radiographs. Methods Standard anteroposterior hip radiographs were obtained from a single tertiary referral center. A total of 538 femoral images were set for the AI model training, including 94 with malignant, 120 with benign, and 324 without tumors. The image data were pre-processed to be optimized for training of the deep learning model. The state-of-the-art convolutional neural network (CNN) algorithms were applied to pre-processed images to perform three-label classification (benign, malignant, or no tumor) on each femur. The performance of the CNN model was verified using fivefold cross-validation and was compared against that of four human doctors. Results The area under the receiver operating characteristic (AUROC) of the best performing CNN model for the three-label classification was 0.953 (95% confidence interval, 0.926–0.980). The diagnostic accuracy of the model (0.853) was significantly higher than that of the four doctors (0.794) (P = 0.001) and also that of each doctor individually (0.811, 0.796, 0.757, and 0.814, respectively) (P<0.05). The mean sensitivity, specificity, precision, and F1 score of the CNN models were 0.822, 0.912, 0.829, and 0.822, respectively, whereas the mean values of four doctors were 0.751, 0.889, 0.762, and 0.797, respectively. Conclusions The AI-based model demonstrated high performance in classifying the presence of bone tumors in the proximal femur on plain radiographs. Our findings suggest that AI-based technology can potentially reduce the misdiagnosis of doctors who are not specialists in musculoskeletal oncology.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation

Park

Kim

et al. 2022

PLoS ONE

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“…The interior part of an eye is imaged through a fundus camera and this image is taken as an input image. The pre-processing is done from the acquired input image [22,23], which means the input image is read and resized by the system, then the readable image data is gone for the removal of different types of noise such as internal noises, external noises, environmental noises, and then it goes for the image smoothing, subsequently the blood vessels are extracted. Based on the developed Graph cut theory, the segmentation of Microaneurysms, Hemorrhages, and Exudates stages of DR is performed after the extraction of blood vessels.…”

Section: Proposed Methodologymentioning

confidence: 99%

Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

Sudha¹,

Srinivasan²,

Devi³

2023

Computer Systems Science and Engineering

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The substantial vision loss due to Diabetic Retinopathy (DR) mainly damages the blood vessels of the retina. These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage, if this problem doesn't exhibit initially, that leads to permanent blindness. So, this type of disorder can be only screened and identified through the processing of fundus images. The different stages in DR are Micro aneurysms (Ma), Hemorrhages (HE), and Exudates, and the stages in lesion show the chance of DR. For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image. The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor (KNN) classifier, Support Vector Machine (SVM) classifier, and Cascaded Rotation Forest (CRF) classifier. Over this classifier, the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity, sensitivity, and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%. Among these Cascaded Rotation Forest (CRF) classifier has more accuracy than others.

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“…Compression technologies have been applied to reduce the volume of medical images for storage and transmission purposes across networks with various bandwidths [ 5 , 6 , 7 , 8 ]. Several lossy and lossless compression methods have been proposed in the literature [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. For lossy methods, some information is lost as a high compression ratio is the primary goal.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

Fahrni

Rotzinger

Nakajo

et al. 2022

JCDD

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Advances in computed tomography (CT) have resulted in a substantial increase in the size of datasets. We built a new concept of medical image compression that provides the best compromise between compression rate and image quality. The method is based on multiple contexts and regions-of-interest (ROI) defined according to the degree of clinical interest. High priority areas (primary ROIs) are assigned a lossless compression. Other areas (secondary ROIs and background) are compressed with moderate or heavy losses. The method is applied to a whole dataset of CT angiography (CTA) of the lower extremity vasculature. It is compared to standard lossy compression techniques in terms of quantitative and qualitative image quality. It is also compared to standard lossless compression techniques in terms of image size reduction and compression ratio. The proposed compression method met quantitative criteria for high-quality encoding. It obtained the highest qualitative image quality rating score, with a statistically significant difference compared to other methods. The average compressed image size was up to 61% lower compared to standard compression techniques, with a 9:1 compression ratio compared with original non-compressed images. Our new adaptive 3D compression method for CT images can save data storage space while preserving clinically relevant information.

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Gaussian Hermite polynomial based lossless medical image compression

Cited by 11 publications

References 24 publications

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation

Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

Three-Dimensional Adaptive Image Compression Concept for Medical Imaging: Application to Computed Tomography Angiography for Peripheral Arteries

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