Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 22
captured using similar films andor exposure levels. Hence, to minimize bias, we used a leave-one-woman-
out strategy in training. The result is shown in a form of confusion matrices.
5. RESULTS AND DISCUSSION
The result is presented in a confusion matrix as in Table 1. The results showed an agreement of 38.33
in comparison with expert assessment and 78.33 when minor classifications deviation is allowed. The
low rate of agreement is below the reported state of the art, which comes partially as a surprise as some of
the state of the art work relies on information extracted from single histograms.
Table 1. Comparison between automatic, histogram based, and expert classification. Within the tables the proportion of dense tissue
is represented as 1: 0, 2: 0-10, 3: 11-25, 4: 26-50, 5: 51- 75, and 6: 76-100.
Instead of taking all six classes into account, for mammographic risk assessment it might be more
appropriate to just take high and low density estimation classes into consideration, which means
that the lower and higher three SCC classes are grouped together. Using such an approach the
developed techniques shows an agreement of 80 with the expert assessment.
We had applied this methodology into MIAS database [10], and an agreement of 55.17 for SCC
and 61.56 for triple MIAS categories were achieved [8]. The latest was similar to those reported by Masek
et al. [7], i.e 62.42 when using an Euclidean distance. Their method is based on direct distance
measures of average histogram of original images for each density class. It should be noted that we used less
data for training due to leave-one-woman-out strategy. Moreover, this is inline with our own single histogram
h
results, which were 61,99 for triple MIAS classification and 57,14 for SCC based
classification. These results might indicate there is little benefit in using the multi-resolution histogram
approach.
It should be noted that our methodology slightly deviated from Hadjidemetriou et al [5]. Their
implementation of the multi-scale approach includes a subsampling step which makes a second normalization
essential. In our case, we only using smoothing stage of the multi-scale approach without the subsampling.
As such the second normalization step is not used. Despite that multi-resolution histogram technique
is claimed to be robust to match either synthetic, Brodatz, or CUReT textures [5], our results could not
confirm its application in mammographic density classification. We would like to investigate whether
this is caused by nature of the mammographic texture patterns andor imaging system effects. Thus,
additional pre-processing to enhance the contrast between fatty and dense tissue, or to incorporate the
X-ray imaging protocol information, are areas of future research.
6. CONCLUSION
We have presented an approach to mammographic density classification, which uses multi-resolution
histogram information. It was shown that the approach was insufficient when compared to the gold standard
provided by an expert radiologist, but when minor classifications errors are allowed it provides a
performance of 78.33. Future work will include texture information.
ACKNOWLEDGMENT
We gratefully acknowledge Dr. Lilian Blot of School of Computing, University of East Anglia, UK
for automatic detection of breast boundary tools.
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Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 24
ANN SOFT SENSOR TO PREDICT QUALITY OF PRODUCT BASED ON TEMPERATURE OR FLOW RATE CORRELATION
Totok R. Biyanto
Engineering Physic Dept. - FTI – ITS Surabaya Kampus ITS Keputih, Sukolilo, Surabaya 60111
Tell: 62 31 5947188 Fax: 62 31 5923626 Email: totokrbep.its.ac.id
ABSTRACT
Analizer has slow respon performance, lack of reliability, and expensive, then inferensial
measurement by using temperature measurement, reflux flow rate and reboiler steam flow rate are
usualy the best way to predict it. This paper will describe Artificial Neural Network ANN soft sensor
capability to predict mole fraction Distillate Xd and mole fraction bottom product Xb at binary
distillation column.
Inferensial measurement could built by using reflux flowrate and reboiler steam flowrate at LV
structure, by using smart positioner feedback signal, or by measuring tray temperature.
Soft sensor which using tray temperature correlation or flow rate correlation have good Root
Mean Square Error RMSE. So, the conclusion is application of ANN soft sensor can build using
temperature or flow rate correlation, depend on control strategy or sensor availability.
Key-words: ANN soft sensor, composition prediction, temperature and flow rate.
1. INTRODUCTION
