Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.

Quantitative analysis system and method for certifying ultrasound medical imaging equipment

Abstract: An analysis system and method provide for quantitatively evaluating image quality characteristics of an ultrasound imaging machine that evaluates at least one image representation of a standard phantom acquired by the image machine. The machine under test by comparing acquired parameters with prestored values, and returning a determined set of image quality indices, along with a single index representing an arithmetic combination of all other image quality indices, which indicate the accuracy of the test image relative to a “gold standard” that has been pre-established for the model of imaging machine under investigation. The system, which includes a computer-programmed set of instructions and data, optionally includes at least one standard phantom. The image quality indices, or metrics, quantitatively represent an evaluation of a test image using a set of relatively subjective criteria that include homogeneity, contrast, signal attenuation and penetration of depth, pin to background ratio in near and far-field, axial and lateral resolution, modulation transfer function, and geometric distortion, and axial and lateral linearity. These image quality indices are determined by specific algorithms and then combined to form an image health index. The image health index and the individual component indices are compared to a gold standard set of indices obtained from an equivalent imaging machine operating under optimum conditions and settings.

Image Scaling Comparison Using Universal Image Quality Index

Abstract: Image interpolation has many applications in computer vision, image processing and biomedical applications. Resampling is required for discrete image manipulations, such as geometric alignment and registration, to improve image quality on display devices or in the field of lossy image compression wherein some pixels are discarded during the encoding process and must be regenerated from the remaining information for decoding. The comparison is done for different interpolation techniques such as nearest neighbor, bilinear and bicubic interpolation and the comparison is done for different interpolation schemes using universal image quality index. In this paper an attempt is made to highlight the universal quality index by comparing with error measures such as MSE and PSNR.

Efficient optic cup detection from intra-image learning with retinal structure priors

Abstract: We present a superpixel based learning framework based on retinal structure priors for glaucoma diagnosis. In digital fundus photographs, our method automatically localizes the optic cup, which is the primary image component clinically used for identifying glaucoma. This method provides three major contributions. First, it proposes processing of the fundus images at the superpixel level, which leads to features more descriptive and effective than those employed by pixel-based techniques, while yielding significant computational savings over methods based on sliding windows. Second, the classifier learning process does not rely on pre-labeled training samples, but rather the training samples are extracted from the test image itself using structural priors on relative cup and disc positions. Third, we present a classification refinement scheme that utilizes both structural priors and local context. Tested on the ORIGA−light clinical dataset comprised of 650 images, the proposed method achieves a 26.7% non-overlap ratio with manually-labeled ground-truth and a 0.081 absolute cup-to-disc ratio (CDR) error, a simple yet widely used diagnostic measure. This level of accuracy is comparable to or higher than the state-of-the-art technique [1], with a speedup factor of tens or hundreds.

Image processing using multiple aspect ratios

Abstract: A system to recognize text, objects, or symbols in a captured image using machine learning models reduces computational overhead by generating a plurality of thumbnail versions of the image at different downscaled resolutions and aspect ratios, and then processing the downscaled images instead of the entire image, or sections of the entire image. The downscaled images are processed to produce a combine feature vector characterizing the overall image. The combined feature vector is processed using the machine learning model.

Learning Structural Regularity for Evaluating Blocking Artifacts in JPEG Images

Abstract: Image degradation damages genuine visual structures and causes pseudo structures. Pseudo structures are usually present with regularities. This letter proposes a machine learning based blocking artifacts metric for JPEG images by measuring the regularities of pseudo structures. Image corner, block boundary and color change properties are used to differentiate the blocking artifacts. A support vector regression (SVR) model is adopted to learn the underlying relations between these features and perceived blocking artifacts. The blocking artifacts score of a test image is predicted using the trained model. Extensive experiments demonstrate the effectiveness of the method.