Region of interest-based image retrieval techniques: a review

Jan, Mardhiyah Md; Zainal, Nasharuddin; Jamaludin, Shahrizan

doi:10.11591/ijai.v9.i3.pp520-528

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…It is identified either using center coordinates with a circular radius surrounding it or by using contours that outline the ROI. 42,43 A binary mask is defined by two-pixel values; the From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Dete… High Yield Medical Reviews first is usually 0, which represents the background, whereas the second value (1 or 255) marks the ROI. This increases the accuracy of AI models and reduces the time and computational power required.…”

Section: Breast Imaging Measuresmentioning

confidence: 99%

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

Jaradat,

Amro,

Hamamreh

et al. 2024

HYMR

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Breast cancer remains a significant global health challenge, necessitating advancements in screening and diagnostic methods for its early detection and treatment. This review explores the role of open-access mammography databases in facilitating research and development in the field of breast cancer detection, particularly through the integration of artificial intelligence techniques such as machine learning and deep learning. In this review, we highlight the open-access databases, including the Digital Database for Screening Mammography (DDSM), the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mini-DDSM, INbreast, Mammographic Image Analysis Society Dataset (MIAS), and the China Mammography and Mastopathy Dataset (CMMD). Each database was analyzed in terms of its composition, features, limitations, and contributions to breast cancer research. In addition, we highlight the importance of open-access databases in enabling collaborative research, improving algorithm development, and enhancing the accuracy and efficiency of breast cancer detection methods computer-aided diagnosis.

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Section: Breast Imaging Measuresmentioning

confidence: 99%

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

Jaradat,

Amro,

Hamamreh

et al. 2024

HYMR

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“…The size (w) and bandwidth (σ) of the filter are determined based on the values of the RM(row, col) and RM(row, col). The function ExtractROI (InputImage, row, col, w) extracts a region of interest 31 from the InputImage with a center (row, col) and size w Â w. The function Gaussian(w, σ) returns a twodimensional isotropic Gaussian weighted average filter with a size w Â w and bandwidth σ. The returned isotropic Gaussian weighted average filter is normalized to ensure that the sum of the filter weights is equal to 1.…”

Section: Ultrasound Image Adaptive Filteringmentioning

confidence: 99%

Empowering ultrasound image filtering precision by reducing speckles and preserving edge cues

Hababeh

Daoud

Al-Najar

2023

Int J Imaging Syst Tech

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Ultrasound speckle is an interference pattern that reduces the quality of medical ultrasound images and decreases the capability of interpreting the medical information included in these images. Therefore, speckle suppression is vital whenever ultrasound imaging is used. In this research, an efficient adaptive ultrasound image filter is proposed to reduce ultrasound speckle while maintaining the edge cues in the image. In particular, the pixels that correspond to tissue boundaries are identified by computing the edge map of ultrasound image, and the noisy pixels are determined by calculating the image radiation. The proposed filtering method is designed to adaptively apply different window sizes and bandwidths based on the edge map and image radiation. The proposed method is employed to process a set of ultrasound images and the filtering performance is assessed using a set of broadly accepted filtering metrics. These metrics aim to evaluate the filtering performance, including the capability of the filter to preserve the edges and maintain the image quality. The performance of the proposed filtering method is compared with eight existing image filtering methods. The experimental results indicated that the proposed filtering method enables effective edge preservation and retains image quality. These results suggest the potential of the proposed filtering method to achieve effective speckle suppression in ultrasound images.

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“…Image thresholding segmentation is commonly used in medical image analysis [23,3,10], in which a lesion region are usually defined as a rectangle, i.e.,…”

Section: Image Segmentation Based Roi Selectionmentioning

confidence: 99%

Non-rigid Interest Object Extraction Using Minimum Convex Coverage

Yang,

Zhang,

Zhang

et al. 2023

Preprint

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Interest object (IO) extraction, or called region of interest (ROI) selection, is a fundamental task in remote sensing (RS), computer vision, and machine learning. Due to the non-rigidity, e.g., shapelessness and color-uncertainty, of interest objects like fires, smokes, clouds, or top-view tree canopies in a RS image, an unideal results usually occurs in IO detection tasks because the model-training is carried on the pixels drawn from an inaccurate ROI, e.g., a bounding-box or convex region. Although it is simple, it fails to capture a complete interest object or sub-object, especially at the requirement of high-precision. In this work, we focus on the non-convex ROI and develop a method for the accurate pixel extraction. To speed pixel extraction, the given non-convex ROI is first decomposed into a minimum convex coverage, and then the ROI pixels can be quickly extracted out from the coverage by using convex operation. For the decomposition , we provide two strategies, named inner decomposition and outer decomposition. Computationally, batch-decision for pixel selection and logical operation for the decomposition are also presented to speed extraction. Theoretically , the existence of minimum coverage for both decompositions is proved. Finally, extensive experimental verifications are carried out on public and our collected RS images, and the results show the superiority of our method in terms of ease of use, rapidity and pixel-precision.

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Region of interest-based image retrieval techniques: a review

Cited by 11 publications

References 27 publications

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection

Empowering ultrasound image filtering precision by reducing speckles and preserving edge cues

Non-rigid Interest Object Extraction Using Minimum Convex Coverage

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