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: LCDs, Signal detection, Medical imaging, Diagnostics, Image quality, Sensors, Mammography, Modulation transfer functions, Image compression, Health sciences
In the soft copy diagnosis, each pixel of the detector is displayed to the correspondent pixel of liquid crystal display
(LCD). But when the image is displayed at the first time, the entire image may be reduced. We examined the influence
that the difference of image reduction rate on LCD exerts on detection performance by using observer performance
experiment. Moreover, to find the best interpolation method, we investigated the several interpolation methods. We
made a simulation image which is similar to Burger phantom. This image consists of 288 signals, each of a different size
and contrast. The matrix size is the same as Phase Contrast Mammography (PCM). We gradated the simulation image by
using an MTF of a geometric blur, and the image was added to the noise image which is uniformly exposed with PCM.
Then the image was reduced by using the nearest-neighbor, the bilinear, and the bicubic methods. The reduction rates
were calculated as the ratios of the number of pixels of LCDs to those of PCM. We displayed the reduced images on
LCD and examined the detection performance. Results of physical evaluation examined before showed that sharpness
and granularity have worsened both in proportion to the reduction rate. The detection performance deteriorated as the
reduction rate becomes high. In the comparison of the interpolation methods, the detection performance of the nearestneighbor
method was worse than those of other interpolation methods. The bilinear method is the most suitable for the
reduction of the image.
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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