Techniques to evaluate the quality of medical images

Pérez-Díaz, Marlen

doi:10.1063/1.4901358

Cited by 3 publications

(4 citation statements)

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“…In medical images, Kocsis et al [25] proposed a set of quantitative measurements related to medical image quality parameters, Perez-Diaz in his work [26] presents a study using some known quality metrics to evaluate the quality in medical images, and Chow and Paramesran [27] publish a review and a classification of medical image quality assessment. An improved SSIM quality for compressed medical images was developed by Kumar et al [28].…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Bouida¹,

Beladgham²,

Bassou³

et al. 2020

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The importance of image compression is now essential during transmission or storage processes in various data applications, especially in medical and biometric systems. To perform the effectiveness of the compression process on images and evaluate degradation caused by this process, image quality assessment becomes an important tool in image services. We note that the objective criteria in image quality depend especially on the image type and image texture composition. The actual tendency is to find metrics making better qualification on errors in compressed images and correlate with the human visual system. This paper presents an investigation to examine and evaluate image compression degradation by the use of a new tendency concept of image quality assessment based on texture and edge analysis. To perform and practice this evaluation, we compress the medical and biometric images using second-generation wavelet compression algorithms and study the degradation of texture information in these images.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Bouida¹,

Beladgham²,

Bassou³

et al. 2020

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“…As such, most sequence development and optimization studies report one or more metrics commonly used by physicists, including SNR, contrast-to-noise ratio (CNR), and modulation transfer function. 47 The correlation between subjective and objective approaches has also been evaluated. 48 To consolidate the strengths of both approaches, we include both subjective and objective metrics relevant to radiotherapy treatment planning in our assessment.…”

Section: Introductionmentioning

confidence: 99%

“…There are efforts to effectively model the HVS with an objective approach, 44 – 46 but optimal solutions have not yet been determined. As such, most sequence development and optimization studies report one or more metrics commonly used by physicists, including SNR, contrast-to-noise ratio (CNR), and modulation transfer function 47 . The correlation between subjective and objective approaches has also been evaluated 48 .…”

Section: Introductionmentioning

confidence: 99%

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform fo

Neck Radiotherapy-MRI Development Cooperative,

Salzillo,

Dresner

et al. 2023

J. Med. Imag.

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To improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T hybrid magnetic resonance imaging/linear accelerator (MR-Linac), three-dimensional (3D), T2-weighted, fat-suppressed magnetic resonance imaging sequences were developed and optimized.Approach: After initial testing, spectral attenuated inversion recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a nonsuppressed, T2-weighted sequence were acquired for five HNC patients using a 1.5T MR-Linac. MR physicists identified persistent artifacts in two of the SPAIR sequences, so the remaining three SPAIR sequences were further analyzed. The gross primary tumor volume, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated using five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences on the basis of qualitative and quantitative metrics.Results: Sequences were analyzed for the signal-to-noise ratio and the contrast-tonoise ratio and compared with fat and muscle, conspicuity, pairwise distance metrics, and segmentor assessments. In this analysis, the nonsuppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but it was superior for the pterygoid muscles. The SPAIR sequence that received the highest combined score among the analysis categories was recommended to Unity MR-Linac users for HNC radiotherapy treatment planning.Conclusions: Our study led to two developments: an optimized, 3D, T2-weighted, fat-suppressed sequence that can be disseminated to Unity MR-Linac users and a robust image quality analysis pathway that can be used to objectively score SPAIR sequences and can be customized and generalized to any image quality

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“…In addition, there are galactophores ducts with abnormalities, such as carcinogenic infiltration [2]. The non-detection of a breast lesion depends not only on the size of the breast or the level of contrast, but also on the sensitivity of the mammograph, as well as the ability of the radiologist who detects them and the conditions under which the radiologist analyze the image [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of Breast Cancer from Digital Mammography Using Deep Learning

López-Cabrera

Rodríguez

Pérez-Díaz

2020

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Breast cancer is the most frequent in females. Mammography has proven to be the most effective method for the early detection of this type of cancer. Mammographic images are sometimes difficult to understand, due to the nature of the anomalies, the low contrast image and the composition of the mammary tissues, as well as various technological factors such as spatial resolution of the image or noise. Computer-aided diagnostic systems have been developed to increase the accuracy of mammographic examinations and be used by physicians as a second opinion in obtaining the final diagnosis, and thus reduce human errors. Convolutional neural networks are a current trend in computer vision tasks, due to the great performance they have achieved. The present investigation was based on this type of networks to classify into three classes, normal, benign and malignant tumour. Due to the fact that the miniMIAS database used has a low number of images, the transfer learning technique was applied to the Inception v3 pre-trained network. Two convolutional neural network architectures were implemented, obtaining in the architecture with three classes, 86.05% accuracy. On the other hand, in the architecture with two neural networks in series, an accuracy of 88.2% was reached.

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Techniques to evaluate the quality of medical images

Cited by 3 publications

References 0 publications

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Evaluation of Textural Degradation in Compressed Medical and Biometric Images by Analyzing Image Texture Features and Edges

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform fo

Classification of Breast Cancer from Digital Mammography Using Deep Learning

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