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Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.

Khazendar S, Sayasneh A, Al-Assam H, Du H, Kaijser J, Ferrara L, Timmerman D, Jassim S, Bourne T - Facts Views Vis Obgyn (2015)

Bottom Line: Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image.This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test).The accuracy improves if texture related LBP features extracted from the images are considered.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.

ABSTRACT

Introduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management.

Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant.

Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected.

Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test).

Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered.

No MeSH data available.


Related in: MedlinePlus

Pre-processing the image before segmentation.The Absolute Difference is a basic image processing operation that takes the absolute value of the difference between the values of the two corresponding pixels I1(i) and I2(i), from the two input images I1 (here is the filtered image) and I2 (here is the negative of the filtered image)r(i) = ∣I1(i) - I2(i)∣where r (i) represents the ith pixel in the result image. We apply the absolute difference operation on the de-noised image from the NL-means filtering step and its negative image. This means thatr(i) = ∣Intensitymax - 2×I(i)∣
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Figure 1: Pre-processing the image before segmentation.The Absolute Difference is a basic image processing operation that takes the absolute value of the difference between the values of the two corresponding pixels I1(i) and I2(i), from the two input images I1 (here is the filtered image) and I2 (here is the negative of the filtered image)r(i) = ∣I1(i) - I2(i)∣where r (i) represents the ith pixel in the result image. We apply the absolute difference operation on the de-noised image from the NL-means filtering step and its negative image. This means thatr(i) = ∣Intensitymax - 2×I(i)∣

Mentions: The original images were received in JPEG digitized format. Each image was pre-processed in three steps as illustrated in Figure 1. Firstly, we used a Non Local mean (NL-means) filter (Buades et al., 2005) to de-noise the image and reduce the negative impact of the Speckle noise (Fig. 2). Then, we conducted a negative transformation of each denoised image in preparation of the last enhancement step (Fig. 1). As a last step of the image pre-processing, we produced the enhanced copy of each image by obtaining the absolute difference between the de-noised image and its negative counterpart. As illustrated in Figure 1, the absolute difference has enhanced the edges in the original B mode image and produced a clearer texture of the cyst (the light grey shades in the resulting image).


Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator.

Khazendar S, Sayasneh A, Al-Assam H, Du H, Kaijser J, Ferrara L, Timmerman D, Jassim S, Bourne T - Facts Views Vis Obgyn (2015)

Pre-processing the image before segmentation.The Absolute Difference is a basic image processing operation that takes the absolute value of the difference between the values of the two corresponding pixels I1(i) and I2(i), from the two input images I1 (here is the filtered image) and I2 (here is the negative of the filtered image)r(i) = ∣I1(i) - I2(i)∣where r (i) represents the ith pixel in the result image. We apply the absolute difference operation on the de-noised image from the NL-means filtering step and its negative image. This means thatr(i) = ∣Intensitymax - 2×I(i)∣
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402446&req=5

Figure 1: Pre-processing the image before segmentation.The Absolute Difference is a basic image processing operation that takes the absolute value of the difference between the values of the two corresponding pixels I1(i) and I2(i), from the two input images I1 (here is the filtered image) and I2 (here is the negative of the filtered image)r(i) = ∣I1(i) - I2(i)∣where r (i) represents the ith pixel in the result image. We apply the absolute difference operation on the de-noised image from the NL-means filtering step and its negative image. This means thatr(i) = ∣Intensitymax - 2×I(i)∣
Mentions: The original images were received in JPEG digitized format. Each image was pre-processed in three steps as illustrated in Figure 1. Firstly, we used a Non Local mean (NL-means) filter (Buades et al., 2005) to de-noise the image and reduce the negative impact of the Speckle noise (Fig. 2). Then, we conducted a negative transformation of each denoised image in preparation of the last enhancement step (Fig. 1). As a last step of the image pre-processing, we produced the enhanced copy of each image by obtaining the absolute difference between the de-noised image and its negative counterpart. As illustrated in Figure 1, the absolute difference has enhanced the edges in the original B mode image and produced a clearer texture of the cyst (the light grey shades in the resulting image).

Bottom Line: Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image.This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test).The accuracy improves if texture related LBP features extracted from the images are considered.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Computing, University of Buckingham, Buckingham, MK18 1EG, U.K.

ABSTRACT

Introduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management.

Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant.

Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected.

Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test).

Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered.

No MeSH data available.


Related in: MedlinePlus