Navegando por Autor "Rocha Neto, Jeová Farias Sales"

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Active contours for overlapping cervical cell segmentation.
(2021) Araujo, Flavio Henrique Duarte de; Silva, Romuere Rodrigues Veloso e; Medeiros, Fátima Nelsizeuma Sombra de; Rocha Neto, Jeová Farias Sales; Oliveira, Paulo Henrique Calaes; Bianchi, Andrea Gomes Campos; Ushizima, Daniela Mayumi
The nuclei and cytoplasm segmentation of cervical cells is a well studied problem. However, the current segmentation algorithms are not robust to clinical practice due to the high computational cost or because they cannot accurately segment cells with high overlapping. In this paper, we propose a method that is capable of segmenting both cytoplasm and nucleus of each individual cell in a clump of overlapping cells. The proposed method consists of three steps: 1) cellular mass segmentation; 2) nucleus segmentation; 3)cytoplasm identification based on an active contour method. We carried out experiments on both synthetic and real cell images. The performance evaluation of the proposed method showed that it was less sensitive to the increase in the number of cells per image and the overlapping ratio against two other existing algorithms. It has also achieved a promising low processing time and, hence, it has the potential to support expert systems for cervical cell recognition.
Hierarchical median narrow band for level set segmentation of cervical cell nuclei.
(2021) Braga, Alan Magalhães; Marques, Régis Cristiano Pinheiro; Medeiros, Fátima Nelsizeuma Sombra de; Rocha Neto, Jeová Farias Sales; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi
This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. These results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.
Hierarchical median narrow band for level set segmentation of cervical cell nuclei.
(2021) Braga, Alan Magalhães; Marques, Régis Cristiano Pinheiro; Medeiros, Fátima Neusizeuma Sombra de; Rocha Neto, Jeová Farias Sales; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi
This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. These results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.