In texture defect detection, the defects can be discriminated according to the distribution ranges of wavelet coefficients
between the normal and defective parts of texture images. In traditional texture defect detection methods, the normal
parts of texture images have to be trained in advance. In this paper, we propose a novel method to automatically
determine the training regions based on the characteristics exhibited by normal and defective texture images. In this way,
the detection error can be reduced because of the avoiding of environmental changes.
Computer-aided diagnosis has become one of the major research subjects in medical imaging and diagnostic radiology.
Hypoxic-ischemic encephalopathy (HIE), remains a serious condition that causes significant mortality and long-term
morbidity to neonates. We adopt self-organizing feature maps to segment the tissues, such as white matter and grey
matter in the magnetic resonance images. The borderline between white matter and grey matter can be found and the
doubtful regions along with the borderline can be localized, then the feature in doubtful regions can be quantified. The
method can assist doctors to easily diagnose whether a neonate is ill with mild HIE.
The effectiveness of Hilbert scan in lossless medical images compression is discussed. In our methods, after coding of
intensities, the pixels in a medical images have been decorrelated with differential pulse code modulation, then the error
image has been rearranged using Hilbert scan, finally we implement five coding schemes, such as Huffman coding,
RLE, lZW coding, Arithmetic coding, and RLE followed by Huffman coding. The experiments show that the case,
which applies DPCM followed by Hilbert scan and then compressed by the Arithmetic coding scheme, has the best
compression result, also indicate that Hilbert scan can enhance pixel locality, and increase the compression ratio
effectively.
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