Rotating Kernel Transformation Research Articles

Two stage contour evolution for automatic segmentation of choroid and cornea in OCT images

Enhanced depth imaging optical coherence tomography (EDI OCT) enables visualization of deeper layers of retina, the segmentation of which can help in the diagnosis of many ophthalmic diseases. Though, a wide variety of segmentation algorithms are available for clinical practice in retinal analysis, segmentation of cornea and choroid are still done manually due to the lack of automated segmentation tools. This paper proposes a multilevel contour evolution approach for segmenting various layers of cornea and choroid. Rotating kernel transformation (RKT) is applied for enhancing the edges followed by two stage contour evolution for detecting edges. Choroid/cornea thickness obtained using our algorithm is compared with manual segmented images traced by ophthalmologists. The algorithm showed good consistency and high correlation with manual segmentation. The results, provided separately for choroid and cornea segmentation prove the efficiency of the proposed method.

Enhanced detection of dentinal lesions in OCT images using the RKT transformation.

Several studies have shown that optical coherence tomography (OCT) can be used to measure the remaining enamel thickness and detect the location of subsurface lesions hidden under the sound enamel. The purpose of this study was to develop algorithms to enhance the visibility of subsurface structures such as hidden occlusal lesions and the dentinal-enamel junction. Extracted teeth with natural occlusal lesions were imaged with OCT with and without added high index fluids. A Rotating Kernel Transformation (RKT) nonlinear image processing filter was applied to PS-OCT images to enhance the visibility of the subsurface lesions under the sound enamel. The filter significantly increased (P<0.05) the visibility of the subsurface lesions.

Comparisons of Speckle Noise Filtering Methods on Interferometric Synthetic Aperture Radar Images

Speckle noise in interferometric synthetic aperture radar (InSAR) phase images seriously degrades the quality of interferogram, disenables interferogram to reflect accurate phase characteristics of the target and increases the difficulty in extracting DEM information of the target area. Therefore, reducing speckle noise by interferogram filtering is a significant step in InSAR processing. First, a noise-included interferometric SAR phase image is simulated based on a terrain model and geometrical parameters of InSAR system. The phase image can be characterized by multilook phase distribution. Then, three interferogram filtering algorithm are explored to remove speckle noise: Goldstain filter, rotating kernel transformation and Lee filter. Proper implementation of three methods is given. Based on experimental results, performances of three different methods are compared. Two aspects need to be comprehensively considered in noise reduction process: the required accuracy in practical application and the processing duration. And also second-time or multiple combined noise reduction is highly recommended.

Comparative Study of Speckle Noise Reduction Approaches for Interferometric Synthetic Aperture Radar Images

Speckle noise appearing in the interferometric SAR (InSAR) phase image degrades the quality of interferogram seriously and makes interferogram reflect the scattering characteristics of the target inaccurately, reducing the capability of extracting DEM information of target areas. Therefore, speckle noise reduction plays a major role in InSAR processing by using interferogram filtering. First, according to a terrain model with the assumed geometrical parameters in InSAR system, the paper simulated an interferometric SAR phase image with noise, which can be characterized by the multilook phase distribution based on the circular Gaussian assumption [1]. Second, the paper explores three interferogram filtering algorithm to remove speckle noise: Goldstain filter, Rotating Kernel Transformation and Lee filter; the proper implementation of three methods is given. Last, the paper discusses about the performance comparatively based on experimental results and gives broad conclusions and presents recommendations.

Improved Rotating Kernel Transformation Based Contourlet Domain Image Denoising Framework.

A contourlet domain image denoising framework based on a novel Improved Rotating Kernel Transformation is proposed, where the difference of subbands in contourlet domain is taken into account. In detail: (1). A novel Improved Rotating Kernel Transformation (IRKT) is proposed to calculate the direction statistic of the image; The validity of the IRKT is verified by the corresponding extracted edge information comparing with the state-of-the-art edge detection algorithm. (2). The direction statistic represents the difference between subbands and is introduced to the threshold function based contourlet domain denoising approaches in the form of weights to get the novel framework. The proposed framework is utilized to improve the contourlet soft-thresholding (CTSoft) and contourlet bivariate-thresholding (CTB) algorithms. The denoising results on the conventional testing images and the Optical Coherence Tomography (OCT) medical images show that the proposed methods improve the existing contourlet based thresholding denoising algorithm, especially for the medical images.

Nonlinear image processing by a rotating kernel transformation

We present a new method for nonlinear image processing that is well suited for hybrid optical-electronic implementation. An input image is convolved with a long, narrow two-dimensional kernel that is rotated, either continuously or discretely, through 360 deg. During rotation the convolution output is monitored, and the maximum and minimum values measured at each point are stored. The processed image is given by an application-dependent function of Max(x, y) and Min(x, y). Setting the output equal to [Max(x, y) - Min(x, y)] enhances straight-line features in noisy, low-contrast images. Better results can be obtained by cascading a Max and a [Max - Min] operation. Numerically calculated examples illustrate the method and compare it with linear filtering.