ary data
it and it
at many
very far
pects of
h video
Travail
Kondo, Hiroshi
COMPUTER AIDED DIAGNOSIS FOR PNEUMOCONIOSIS
RADIOGRAPHS’ USING NEURAL NETWORK
Hiroshi KONDO, Lifeng ZHANG, and Takaharu KODA
Kyushu Institute of Technology JAPAN
kondou@ele.kyutech.ac.jp
KEY WORDS: X-ray photo, Pneumoconiosis, Neural Network.
ABSTRACT
Computer aided diagnosis for pneumoconiosis using neural network is presented. The rounded opacities in the
pneumoconiosis X-ray photo are picked up by a back propagation (BP) — neural network with several typical training
patterns. The training patterns from 0.6 mm@ to 30 mm¢ are made by simple circles. The neck problem for an
automatic pneumoconiosis diagnosis has been to reject the unnecessary part like ribs and vessel's shades. In this paper
such an unnecessary part is rejected well by adding several output neurons for own presenting neural network . These
neurons are used only for picking unnecessary parts up. The input for the neural network is 30 ^ 30 pixel image which
is quarried succeedingly from the bi-level ROI (region of interest) image with the size 500” 500 pixel. The new
technique called moving normalization is developed here in order to made an appropriate bi-level ROI image. The total
evaluation is done from the size and figure categorization and density categorization. many simulation examples show
that the proposed method gives much reliable results than traditional ones.
1. INTRODUCTION
In recent years computer-aided diagnosis is very popular in a medical field. The aim of computer-aided diagnosis is to
alert the radiologist by indicating potential lesions and/or providing quantitative information as a second opinion. Since
the middle of 1980, a number of computerized schemes for computer-aided diagnosis have been developed for chest
radiography, mammography, angiography, and bone radiography. Especially in chest radiography, many computerized
schemes have been applied to the detection and classification of pneumoconiosis, because the quantitative analysis of it
has been required from the view point of workmen's accident compensation insurance. Pneumoconiosis is a lung
disease caused by, for example, the long term inhalation of coal dust and the local tissue reaction to the accumulated
dust particles. The first radiological symptom in the development of simple pneumoconiosis is the appearance of small
opacities, either rounded or somewhat irregular, in the chest X-rays. According to the profusion of small opacities,
categories 0-3 have been established to indicate the severity of the disease where category 0 means normal case and
category 3 means very numerous small opacities. The early studies of computer pneumoconiosis analysis have been
done by several groups (Morishita, Yanagisawa, Katsuragauia, Doi, MacMabon, Nakamori, Sasaki, and Fennessy. In
their studies the texture analysis for the X-ray photos has been taken. Recently, however, the study trends toward the
detection of the small rounded opacity itself because of the extremely development of the computer hard and software.
They have utilized a special filtering for dropping off the unnecessary part like rib shade in the X-rays. The performance
of the filtering is not satisfied sometimes due to the vagueness of the X-rays. In this paper the neural network is
introduced to pick the rounded opacities up from the X-ray photo with no filtering. A neural network is powerful for
pattern matching. Here a back propagation neural network with three layers is used.
2. PNEUMOCONIOSIS
Pneumoconiosis is one of the serious lung occupational disease. Hence the diagnosis result of a medical doctor gives a
big implication for the workmen's accident compensation insurance. Even such doctor's diagnosis results, however, are
not often consistent with each other. For this reason it has been required that the quantitative analysis of
pneumoconiosis is established. According to the classification scheme of the International Labor Office (ILO), there are
two kind of categories : one is number and area density classification and the other is size-figure one. The former one
has three ranks from 0 to 3, where 0 means normal case and rank 3 means very serious case. The size-figure
classification has also three types as P, Q, and R, where P means the equivalent diameter d of the opacity is
noooooooo0000000000000less than or equal to 1.5 mm, Q means 1.5 mm < des 3.0 mm, and R means 3.0
< de 10.0 mm. Figure 1 shows allnormalized X-ray photo with 3000“ 3000” 8 bit. The normalization is made by
setting the minimum gray level 0 (black) and the maximum value 255 (white). The other gray levels are transformed
linearly between the above two values. Figure 1 is the photo for (3,P) category. Usually in the analysis of a
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B5. Amsterdam 2000. 453