Current methods of radiological displays provide only grayscale images of mammograms. The limitation of the image space to grayscale provides only luminance differences and textures as cues for object recognition within the image. However, color can be an important and significant cue in the detection of shapes and objects. Increasing detection ability allows the radiologist to interpret the images in more detail, improving object recognition and diagnostic accuracy. Color detection experiments using our stimulus system, have demonstrated that an observer can only detect an average of 140 levels of grayscale. An optimally colorized image can allow a user to distinguish 250 - 1000 different levels, hence increasing potential image feature detection by 2-7 times. By implementing a colorization map, which follows the luminance map of the original grayscale images, the luminance profile is preserved and color is isolated as the enhancement mechanism. The effect of this enhancement mechanism on the shape, frequency composition and statistical characteristics of the Visual Evoked Potential (VEP) are analyzed and presented. Thus, the effectiveness of the image colorization is measured quantitatively using the Visual Evoked Potential (VEP).
Many objects in our visual field compete for neural representation. Both bottom-up, sensory-driven processes (luminance detection) as well as top-down mechanisms (attention and familiarity) can affect the result of this competition. In this study, visual evoked potentials were used to measure the changes induced by both stimulus variables and attention processes. The stimulus set consisted of a grayscale sine wave grating pattern with different degrees of spatially random noise. This stimulus set was generated using the ALOPEX optimization algorithm. This algorithm generated a series of sequential images while converging from a completely random noise pattern to the sine wave grating pattern template. All of the patterns in the stimulus set were normalized for average luminance during the ALOPEX convergence process. Additionally, the stimulus content of each pattern was quantified using a number of image processing algorithms including space-averaged global contrast, image entropy, central moments, 2D Fourier transform, and 2D wavelet transform. The visual evoked potentials were recorded using the same pattern set for different attention states of the subjects. The results presented demonstrate the contrasting affects of noise and attention on both the time and frequency components of the visual evoked potential recorded from different lobes of the brain.
Recent advances in image and signal processing have created a new challenging environment for biomedical engineers. Methods that were developed for different fields are now finding a fertile ground in biomedicine, especially in the analysis of bio-signals and in the understanding of images. More and more, these methods are used in the operating room, helping surgeons, and in the physician's office as aids for diagnostic purposes. Neural Network (NN) research on the other hand, has gone a long way in the past decade. NNs now consist of many thousands of highly interconnected processing elements that can encode, store and recall relationships between different patterns by altering the weighting coefficients of inputs in a systematic way. Although they can generate reasonable outputs from unknown input patterns, and can tolerate a great deal of noise, they are very slow when run on a serial machine. We have used advanced signal processing and innovative image processing methods that are used along with computational intelligence for diagnostic purposes and as visualization aids inside and outside the operating room. Applications to be discussed include EEGs and field potentials in Parkinson's disease along with 3D reconstruction of MR or fMR brain images in Parkinson's patients, are currently used in the operating room for Pallidotomies and Deep Brain Stimulation (DBS).
Analysis of variance for coefficient of variation is performed under various noise levels and moment orders. As a measure for response stability for features in a pattern recognition system, the coefficient of variation is analyzed and compared among various moment sets, such as, the geometric moments, Legendre moments, complex moments, rotational moments and Zernike moments. A convenient definition for binary segmented images is introduced in order to quantify the level of the noise in the noisy patterns. Traditional two-way table data analysis is carried out to fit the nearly additively structured data and the analysis of variance is done on the fittings (row and column effects). A simple summary for the fittings is displayed by boxplots to show the distribution of effects due to the various noise levels and the moment orders. Among the different moment sets, the Zernike moments are shown to be optimal in the sense that Zernike moments are reliable features with the property of least varying response under various noise and orders.
Conference Committee Involvement (1)
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
5 August 2003 | San Diego, California, United States
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