Multi-scale lacunarity as an alternative to quantify and diagnose the behavior of prostate cancer

Neves, Leandro Alves; Nascimento, Marcelo Zanchetta do; Oliveira, Domingos Lucas Latorre de; Martins, Alessandro Santana; Godoy, Moacir Fernandes de; Arruda, Pedro Francisco Ferraz de; Neto, Dalisio de Santi; Machado, J.M.

doi:10.1016/j.eswa.2014.02.048

Cited by 22 publications

(10 citation statements)

References 51 publications

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“…This is due to the high similarity between classes of leaf textures. This also suggest that lacunarity measurement was better performed for data with a few number of classes, like what have been done in previous study [14][15][16][17][18]. At first, we use only one input value, but the result is not quite satisfying.…”

Section: Lacunarity Analysismentioning

confidence: 85%

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Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Muchtar

Suciati

Fatichah

2016

Jurnal Ilmu Komputer dan Informasi

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Plants play important roles for the existence of all beings in the world. High diversity of plant's species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant's leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implemented to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy. Keywords: leaf classification, fractal dimension, lacunarity, box counting, gliding box AbstrakTumbuhan memegang peranan penting dalam kehidupan manusia. Tingginya keberagaman spesies tumbuhan membuat metode pengamatan manual dalam klasifikasi daun menjadi semakin sulit. Dimensi fraktal merupakan deskriptor bentuk dan tekstur yang mampu mendeskripsikan kompleksitas dari suatu objek dalam bentuk dimensi pecahan. Di sisi lain, lacunarity adalah deskriptor tekstur berbasis fraktal yang mampu mendeskripsikan heterogenitas dari citra tekstur. Namun lacunarity belum cukup dieksplorasi dalam banyak kasus dan belum ada usaha yang cukup signifikan dalam mengkombinasikan dimensi fraktal dan lacunarity dalam bidang klasifikasi tumbuhan secara otomatis. Penelitian ini berfokus pada ekstraksi dan kombinasi fitur dimensi fraktal dan lacunarity untuk meningkatkan akurasi klasifikasi. Metode box counting diterapkan untuk memperoleh dimensi fraktal dari bentuk pinggiran dan urat daun, sementara metode gliding box diterapkan untuk memperoleh fitur lacunariy dari tekstur daun. menggunakan 626 citra daun dari flavia, percobaan dilakukan dengan menganalisis performa dari kedua fitur dengan mempertimbangkan ukuran kotak r yang paling optimal. Klasifikasi dengan support vector machine menunjukkan bahwa hasil kombinasi kedua fitur mampu mencapai rata-rata akurasi hingga 93.92%.

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Section: Lacunarity Analysismentioning

confidence: 85%

“…While high lacunarity indicates that the texture is heterogeneous. Lacunarity has been applied in several areas of texture-based research, such as in the field of spatial data mapping [14,15], medical [16,17], and the agricultural industry [18].…”

Section: Introductionmentioning

confidence: 99%

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Muchtar

Suciati

Fatichah

2016

Jurnal Ilmu Komputer dan Informasi

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“…Lacunarity could also be used to quantify the severity of disease or to differentiate benign from malignant tumors. Literature presents various examples of the use of lacunarity in other scenarios, such as to assess osteoporosis [12], microvascular morphology [26], to analyze the behavior of prostate cancer [27] and breast tumor lesions [28], and to measure cell cancer behavior [29].…”

Section: Discussionmentioning

confidence: 99%

Fibrous Dysplasia Characterization Using Lacunarity Analysis

et al. 2015

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Fibrous dysplasia (FD) is a developmental anomaly in which the normal medullary space of the affected bone is replaced by fibro-osseous tissue. This condition is typically encountered in adolescents and young adults. It affects the maxillofacial region and it can often cause severe deformity and asymmetry. Therefore, accurate diagnosis is critical to determine the appropriate treatment of each case. In this sense, computed tomography (CT) is a relevant resource among the imaging techniques for correct diagnosis of this condition. Thus, in this paper, we propose to analyze fibrous dysplasia through its texture pattern. To accomplish this task, we propose to use lacunarity analysis, a multiscale method for describing patterns of spatial dispersion. Results indicated lower lacunarity values for fibrous dysplasia in comparison to normal bone samples, an indication that their texture images are more homogeneous, and a high separability between the classes when using principal component analysis (PCA) and decision trees for statistical analysis.

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“…While high value of lacunarity indicates that the texture is heterogeneous. Lacunarity has been applied in several areas of research-based texture, such as the mapping of spatial data [12], medical [13], and the agricultural industry [14].…”

Section: Introductionmentioning

confidence: 99%

Fractal-based texture and HSV color features for fabric image retrieval

Suciati

Herumurti

Wijaya

2015

2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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Wide range of products such as clothing, bed linen, curtains, and shoes, use fabrics as main raw material. Fabrics have various types of materials, colors and patterns. Harmony in combiningthe various types of fabrics will affect the beauty of the resulted product. A system that can be used to retrieve some fabrics similar to a fabrics sample automatically will facilitate the combining process in creating a product. In this study, a fabrics image retrieval system using combination of fractal-based texture feature and HSV color feature is developed. The Canberra Distance is used to measure similarity between features vectors. The experiment which is done using two kinds of fabrics image datasets, i.e. "batik" and "common", gives average recall 94% and 92%, respectively.IndexTerms-Image retrieval, fractal-based texture, HSV color.

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Multi-scale lacunarity as an alternative to quantify and diagnose the behavior of prostate cancer

Cited by 22 publications

References 51 publications

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Fractal Dimension and Lacunarity Combination for Plant Leaf Classification

Fibrous Dysplasia Characterization Using Lacunarity Analysis

Fractal-based texture and HSV color features for fabric image retrieval

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