Nomenclatural notes and morphological observations on Malva subovata s.l. (Malvaceae)

DUILIO IAMONICO

doi:10.11646/phytotaxa.275.1.3

Nomenclatural notes and morphological observations on Malva subovata s.l. (Malvaceae)

Phytotaxa ◽

10.11646/phytotaxa.275.1.3 ◽

2016 ◽

Vol 275 (1) ◽

pp. 23 ◽

Cited By ~ 3

Author(s):

DUILIO IAMONICO

Keyword(s):

Morphological Features ◽

Infraspecific Classification ◽

Infraspecific Taxa

Nomenclatural notes and morphological observations on Malva subovata sensu lato (Malvaceae) are discussed. The names Axolpha wigandii, L. africana by Cavanilles, L. africana by Miller, L. maritima, L. maritima var. purpurea, L. maritima var. trilobata, L. rupestris, and Lavatera subovata are lectotypified upon specimens preserved in the Herbaria BM, COI, G-DC, LY, MPU, and upon illustrations by Cavanilles and Gouan, while L. gallica, L. rotundifolia, and Olbia canescens are illegitimate names. Cavanilles’ L. africana, L. maritima, and A. wigandii are confirmed to be synonyms of L. subovata. Miller’s L. africana, a prevoiusly published homonym of Cavanilles’ L. africana, refers to another species. L. gallica, L. maritima var. purpurea, L. maritima var. trilobata, L. rotundifolia, and Olbia canescens are to be considered synonyms of L. subovata s.s. On the basis of morphological features and distribution areas, the current infraspecific classification of M. subovata s.l. (three subspecies) cannot be retained, and the better choice is the recognition of only two infraspecific taxa (subsp. subovata and subsp. bicolor), while the subsp. rupestris is to be treated as a synonym of the autonymic subspecies.

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Instability of morphological features used for classification of Thymus pulegioides infraspecific taxa

Acta Botanica Hungarica ◽

10.1556/abot.48.2006.3-4.9 ◽

2006 ◽

Vol 48 (3-4) ◽

pp. 345-360 ◽

Cited By ~ 5

Author(s):

K. Ložiene

Keyword(s):

Morphological Features ◽

Infraspecific Taxa

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Supervised Machine Learning Classification of Human Sperm Head Based on Morphological Features

10.1007/978-3-030-75945-2_9 ◽

2021 ◽

pp. 177-191

Author(s):

Natalia V. Revollo ◽

G. Noelia Revollo Sarmiento ◽

Claudio Delrieux ◽

Marcela Herrera ◽

Rolando González-José

Keyword(s):

Machine Learning ◽

Human Sperm ◽

Sperm Head ◽

Morphological Features ◽

Supervised Machine Learning ◽

Machine Learning Classification

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A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/5894010 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Mengwan Wei ◽

Yongzhao Du ◽

Xiuming Wu ◽

Qichen Su ◽

Jianqing Zhu ◽

...

Keyword(s):

Breast Tumor ◽

Texture Features ◽

Morphological Features ◽

High Dimensional ◽

Svm Classifier ◽

Ultrasound Images ◽

Malignant Breast Tumor ◽

Malignant Breast ◽

Low Dimensional

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

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