Abstract
Blood cancer is an umbrella term for cancers that affect the blood, bone marrow and lymphatic system. There are three main groups of blood cancer: leukemia, lymphoma and myeloma. Some types are more common than others. In this paper, a new image transform based on geometric mean properties of integral values in both horizontal and vertical image directions is proposed for leukemia cancer cell classification. Available classification methods using the classical feature extraction methods which are sensitive to rotation and deformation of the blood cells. The new transform is based on geometric mean projection, which -unlike other image transforms, such as Radon transform- is not considered all signals in an image with the same signal acquisition rate. Instead, it is general and thus applicable to all capturing signal functions to achieve sufficient invariant features. The geometric mean projection transforms (GMPT) guarantees that the detector only extracts the highly informative information from the object to achieve an invariant feature vector for an accurate classification process. This method has been used as cancer cell identification using microscopic Imagery analysis in this study. Dissimilarity metric calculation and shape analysis by using image transform has been used to extract the feature vectors of the imagery. Then, the accumulated feature vectors have been classified to different classes by using artificial neural network (ANN). The proposed technique has been evaluated in the standard images sourced from USIM, Malaysia. The evaluation results indicate the robustness of the technique in different types of images available in the dataset.
| Original language | English |
|---|---|
| Pages (from-to) | 223-234 |
| Number of pages | 12 |
| Journal | Pertanika Journal of Science and Technology |
| Volume | 25 |
| Issue number | S6 |
| Publication status | Published - 1 Jun 2017 |
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Keywords
- Cancer cell classification image transform
- Image processing
- Pattern recognition
ASJC Scopus subject areas
- Computer Science(all)
- Chemical Engineering(all)
- Environmental Science(all)
- Agricultural and Biological Sciences(all)
Cite this
Blood cancer cell classification based on geometric mean transform and dissimilarity metrics. / Kahaki, Seyed Mostafa Mousavi; Nordin, Md. Jan; Ismail, Waidah; Zahra, Sophia Jamila; Hassan, Rosline.
In: Pertanika Journal of Science and Technology, Vol. 25, No. S6, 01.06.2017, p. 223-234.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Blood cancer cell classification based on geometric mean transform and dissimilarity metrics
AU - Kahaki, Seyed Mostafa Mousavi
AU - Nordin, Md. Jan
AU - Ismail, Waidah
AU - Zahra, Sophia Jamila
AU - Hassan, Rosline
PY - 2017/6/1
Y1 - 2017/6/1
N2 - Blood cancer is an umbrella term for cancers that affect the blood, bone marrow and lymphatic system. There are three main groups of blood cancer: leukemia, lymphoma and myeloma. Some types are more common than others. In this paper, a new image transform based on geometric mean properties of integral values in both horizontal and vertical image directions is proposed for leukemia cancer cell classification. Available classification methods using the classical feature extraction methods which are sensitive to rotation and deformation of the blood cells. The new transform is based on geometric mean projection, which -unlike other image transforms, such as Radon transform- is not considered all signals in an image with the same signal acquisition rate. Instead, it is general and thus applicable to all capturing signal functions to achieve sufficient invariant features. The geometric mean projection transforms (GMPT) guarantees that the detector only extracts the highly informative information from the object to achieve an invariant feature vector for an accurate classification process. This method has been used as cancer cell identification using microscopic Imagery analysis in this study. Dissimilarity metric calculation and shape analysis by using image transform has been used to extract the feature vectors of the imagery. Then, the accumulated feature vectors have been classified to different classes by using artificial neural network (ANN). The proposed technique has been evaluated in the standard images sourced from USIM, Malaysia. The evaluation results indicate the robustness of the technique in different types of images available in the dataset.
AB - Blood cancer is an umbrella term for cancers that affect the blood, bone marrow and lymphatic system. There are three main groups of blood cancer: leukemia, lymphoma and myeloma. Some types are more common than others. In this paper, a new image transform based on geometric mean properties of integral values in both horizontal and vertical image directions is proposed for leukemia cancer cell classification. Available classification methods using the classical feature extraction methods which are sensitive to rotation and deformation of the blood cells. The new transform is based on geometric mean projection, which -unlike other image transforms, such as Radon transform- is not considered all signals in an image with the same signal acquisition rate. Instead, it is general and thus applicable to all capturing signal functions to achieve sufficient invariant features. The geometric mean projection transforms (GMPT) guarantees that the detector only extracts the highly informative information from the object to achieve an invariant feature vector for an accurate classification process. This method has been used as cancer cell identification using microscopic Imagery analysis in this study. Dissimilarity metric calculation and shape analysis by using image transform has been used to extract the feature vectors of the imagery. Then, the accumulated feature vectors have been classified to different classes by using artificial neural network (ANN). The proposed technique has been evaluated in the standard images sourced from USIM, Malaysia. The evaluation results indicate the robustness of the technique in different types of images available in the dataset.
KW - Cancer cell classification image transform
KW - Image processing
KW - Pattern recognition
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UR - http://www.scopus.com/inward/citedby.url?scp=85044225461&partnerID=8YFLogxK
M3 - Article
VL - 25
SP - 223
EP - 234
JO - Pertanika Journal of Science and Technology
JF - Pertanika Journal of Science and Technology
SN - 0128-7680
IS - S6
ER -