With recent developments, digital mammograms can be obtained with a small pixel size, i.e., high resolution; however,
the matrix size increases. Therefore, when the image is thinned out, image information is lost when the image is
displayed on a liquid crystal display (LCD). To resolve this issue, we have developed a super high resolution liquid
crystal display (SHR-LCD) by using a novel resolution enhancement technology for independent subpixel driving (ISD)
with three subpixels in each pixel element. However, the lack of image information caused by thinning of the image
cannot be ignored because the matrix size of a phase contrast mammogram (PCM) is very large as compared to that of a
conventional mammogram. We obtained noise and edge images by using the geometrical layouts of the PCM (7080 x
9480). We measured the Wiener spectrum (WS), modulation transfer function (MTF), and noise-equivalent number of
quanta (NEQ) of the images reduced by the nearest-neighbor, bilinear, and bicubic (sharpness and smooth) interpolations.
The reduction rate was approximately 0.14. We measured the WS and MTF when the PCM image was displayed on a
5-megapixel (MP) and 15-MP LCD. The bilinear interpolation technique gave the best image quality. The image quality
was further improved by using a 15-MP SHR-LCD.
KEYWORDS: Signal to noise ratio, Signal detection, Visualization, Image quality, Modulation transfer functions, Interference (communication), Spatial frequencies, Eye models, Visual process modeling, Image visualization
The effects of imaging parameters on detectability have not yet been clarified. Therefore, we investigated the
usefulness of signal-to-noise ratios (SNRs) considered as human visual characteristics, such as the visual spatial
frequency response and the internal noise in the eye-brain system.
We examined the amplitude model (SNRa), matched filter model (SNRm), and internal noise model (SNRi) to study
the relationship between these SNRs and the visual image quality for signal detection. The test images were simulated by
the superimposition of low-contrast signals on a uniform noisy background. The SNRs were obtained for 15 imaging
cases with various signal sizes, signal contrasts, exposure levels, and number of acrylic plates used as breast phantoms.
The SNRs were calculated by measuring the spatial frequency characteristics of the signal, modulation transfer
function (MTF) of the system, display MTF, and overall Wiener spectrum (WS).
In the perceptual evaluation, we applied the 16-alternative forced choice (16-AFC) method. The signal detectability
was defined as the number of detected signals divided by the total number of signals. We studied the relationship
between SNR and signal detectability using Spearman's rank correlation coefficient.
The correlation coefficient of SNRi was 0.93, making it the highest among the three SNR types. That of SNRm was
0.91; it correlated at the same level as SNRi although it is not considered human visual characteristics. That of SNRa
was 0.45. SNRi, which incorporated the visual characteristics, explained the visual image quality well.
Soft-copy diagnosis of medical images is currently widespread. Because the pixel size of a digital mammogram is very
small, the matrix size is extremely large. Especially the matrix size of phase contrast mammography (PCM) is very large
compared with a conventional mammography. When such an image is displayed on a liquid crystal display (LCD), it is
displayed as a reduction image. Therefore, it is necessary to use an appropriate reduction rate and an interpolation
method such that the reduction processing does not influence the diagnosis. We obtained a uniform image exposure and
measured the noise power spectrum (NPS) of the image reduced by using the nearest neighbor, bilinear, and bicubic
methods with several reduction rates. Our results showed that the best interpolation method was the bilinear method.
Moreover, the NPS value increased by a factor of the square of the inverse of the reduction rate.
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