Active contour bilateral filter for breast lesions segmentation on ultrasound images

Nugroho, Anan; Nugroho, Hanung Adi; Choridah, Lina

doi:10.1109/icsitech.2015.7407773

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…[23][24][25] The BF was introduced by Tomasi and Maduchi 26 in which every value of output pixel in the whole image is a weighted Gaussian average of its neighbors in both intensity and spatial range. Based on our preliminary studies, 27,28 the BF can effectively increase homogeneity, enhance object boundary, and preserve useful information in the US images. Moreover, the newest modified BF is fast and robust at large noise levels, thus making it work better and have more power.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial active contour bilateral filter for ultrasound image segmentation

et al. 2020

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Purpose: Utilization of computer-aided diagnosis (CAD) on radiological ultrasound (US) imaging has increased tremendously. The prominent CAD applications are found in breast and thyroid cancer investigation. To make appropriate clinical recommendations, it is important to accurately segment the cancerous object called a lesion. Segmentation is a crucial step but undoubtedly a challenging problem due to various perturbations, e.g., speckle noise, intensity inhomogeneity, and low contrast. Approach: We present a combinatorial framework for US image segmentation using a bilateral filter (BF) and hybrid region-edge-based active contour (AC) model. The BF is adopted to smooth images while preserving edges. Then the hybrid model of region and edge-based AC is applied along the scales in a global-to-local manner to capture the lesion areas. The framework was tested in segmenting 258 US images of breast and thyroid, which were validated by manual ground truths. Results: The proposed framework is accessed quantitatively based on the overlapping values of the Dice coefficient, which reaches 90.05 AE 5.81%. The evaluation with and without the BF shows that the enhancement procedure improves the framework well. Conclusions: The high performance of the proposed method in our experimental results indicates its potential for practical implementations in CAD radiological US systems.

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Section: Introductionmentioning

confidence: 99%

Combinatorial active contour bilateral filter for ultrasound image segmentation

et al. 2020

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“…Otherwise, image label can be removed by using adaptive median filter as shown in [4,5]. For segmentation, many research works have proven that the active contour had well performance to be applied in ultrasound images [6][7][8][9][10]. For classification, analysis of texture features such as histogram statistic, grey level co-occurrence matrices [11].…”

Section: Introductionmentioning

confidence: 99%

Shape analysis for classification of breast nodules on digital ultrasound images

Nugroho¹,

Yusufiyah²,

Adji³

et al. 2019

IJEECS

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<span>One of the imaging modalities for early detection of breast cancer malignancy is ultrasonography (USG). The malignancy can be analysed from the characteristic of nodule shape. This study aims to develop a method for classifying the shape of breast nodule into two classes, namely regular and irregular classes. The input image is pre-processed by using the combination of adaptive median filter and speckle reduction bilateral filtering (SRBF) to reduce speckle noises and to eliminate the image label. Afterwards, the filtered image is segmented based on active contour followed by feature extraction process. Nine extracted features, i.e. roundness, slimness and seven features of invariant moments, are used to classify nodule shape using multi-layer perceptron (MLP). The performance of the proposed method is evaluated using 105 breast nodule images which comprise of 57 regular and 48 irregular nodule images. The results of classification process achieve the level of accuracy, sensitivity and specificity at 96.20%, 97.90% and 94.70%, respectively. These results indicate that the proposed method successfully classifies the breast nodule images based on shape analysis.</span>

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“…Although many CAD algorithms have been explored in 2D HHUS, [12][13][14][15][16][17][18] only a few studies have been proposed for 3D ABUS volumes. For instance, Diaz et al 19 extended the segmented method developed by Pons et al 15 for 2D HHUS based on Markov Random Field-Maximum a Posteriori to 3D volumes, showing a decrease in performance from 0.75 to 0.55, in terms of Dice Similarity Coefficient (DSC).…”

Section: Introductionmentioning

confidence: 99%

Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis

et al. 2017

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Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact ([Formula: see text]) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of [Formula: see text] with a mean False Positive ratio [Formula: see text] has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is [Formula: see text] ([Formula: see text]). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient [Formula: see text] ([Formula: see text]) for prior and [Formula: see text] ([Formula: see text]) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.

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Active contour bilateral filter for breast lesions segmentation on ultrasound images

Cited by 7 publications

References 11 publications

Combinatorial active contour bilateral filter for ultrasound image segmentation

Combinatorial active contour bilateral filter for ultrasound image segmentation

Shape analysis for classification of breast nodules on digital ultrasound images

Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis

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